Tootfinder

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study
Hanna Woloszyn, Benjamin Gagl
https://arxiv.org/abs/2508.13769 https://

Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study
The role of large language models (LLMs) in education is increasing, yet little attention has been paid to whether LLM-generated text resembles child language. This study evaluates how LLMs replicate child-like language by comparing LLM-generated texts to a collection of German children's descriptions of picture stories. We generated two LLM-based corpora using the same picture stories and two prompt types: zero-shot and few-shot prompts specifying a general age from the children corpus. We con…

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Test-time Corpus Feedback: From Retrieval to RAG
Mandeep Rathee, Venktesh V, Sean MacAvaney, Avishek Anand
https://arxiv.org/abs/2508.15437 https://arxiv.o…

Test-time Corpus Feedback: From Retrieval to RAG
Retrieval-Augmented Generation (RAG) has emerged as a standard framework for knowledge-intensive NLP tasks, combining large language models (LLMs) with document retrieval from external corpora. Despite its widespread use, most RAG pipelines continue to treat retrieval and reasoning as isolated components, retrieving documents once and then generating answers without further interaction. This static design often limits performance on complex tasks that require iterative evidence gathering or hig…

The U.S. Department of Housing and Urban Development is preparing to
💥shut down seven major investigations and cases concerning alleged housing discrimination and segregation, including
⚠️ some where the agency already found civil rights violations, according to HUD records obtained by ProPublica.
The high-profile cases involve allegations that state and local governments across the South and Midwest illegally discriminated against people of color
by placing industrial…

Trump Administration Prepares to Drop Seven Major Housing Discrimination Cases
Federal housing officials spent years investigating cities from Chicago to Memphis to Corpus Christi for putting industrial plants and unwanted facilities in poor, nonwhite neighborhoods. Now, under Trump, the agency plans to drop the cases.

Guidelines for the Enhancement of the Corpus and the Verismo Vocabulary
Michael Bassi, Giovanni Salucci
https://arxiv.org/abs/2508.15645 https://arxiv.org/…

Guidelines for the Enhancement of the Corpus and the Verismo Vocabulary
VIVer is a digital lexicography project with historical-literary and historical-linguistic aims that can be considered a case study of a Digital Humanities project. This paper presents the IT choices made to promote the dissemination and enhancement of the results, analysing the issues and advantages for wider adoption, beyond the specific VIVer project, serving as a model and inspiration for future projects.

I read 'The Public Interest Corpus Update – NYC Edition'. More work on the project's principles and goals, research and library service use cases, and thinking ahead to prospective year 1-3 and year 4-6 activities https://publicinterestcorpus.org/the-p

ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue
Jeongwoo Kang, Maria Boritchev, Maximin Coavoux
https://arxiv.org/abs/2508.12819 https://arxiv…

ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue
We present our work to build a French semantic corpus by annotating French dialogue in Abstract Meaning Representation (AMR). Specifically, we annotate the DinG corpus, consisting of transcripts of spontaneous French dialogues recorded during the board game Catan. As AMR has insufficient coverage of the dynamics of spontaneous speech, we extend the framework to better represent spontaneous speech and sentence structures specific to French. Additionally, to support consistent annotation, we prov…

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Fat Bear Week is coming early this year. The annual online competition that normally starts in early October will instead start on Sept. 23.

Systematic Review Of Collaborative Learning Activities For Promoting AI Literacy
Ashish Hingle, Aditya Johri
https://arxiv.org/abs/2508.15111 https://arxiv…

Systematic Review Of Collaborative Learning Activities For Promoting AI Literacy
Improving artificial intelligence (AI) literacy has become an important consideration for academia and industry with the widespread adoption of AI technologies. Collaborative learning (CL) approaches have proven effective for information literacy, and in this study, we investigate the effectiveness of CL in improving AI knowledge and skills. We systematically collected data to create a corpus of nine studies from 2015-2023. We used the Interactive-Constructive-Active-Passive (ICAP) framework to…

Best Practices and Considerations for Child Speech Corpus Collection and Curation in Educational, Clinical, and Forensic Scenarios
John Hansen, Satwik Dutta, Ellen Grand
https://arxiv.org/abs/2507.12870

Best Practices and Considerations for Child Speech Corpus Collection and Curation in Educational, Clinical, and Forensic Scenarios
A child's spoken ability continues to change until their adult age. Until 7-8yrs, their speech sound development and language structure evolve rapidly. This dynamic shift in their spoken communication skills and data privacy make it challenging to curate technology-ready speech corpora for children. This study aims to bridge this gap and provide researchers and practitioners with the best practices and considerations for developing such a corpus based on an intended goal. Although primarily foc…

An LLM ASP Workflow for Joint Entity-Relation Extraction
Trang Tran, Trung Hoang Le, Huiping Cao, Tran Cao Son
https://arxiv.org/abs/2508.12611 https://a…

An LLM + ASP Workflow for Joint Entity-Relation Extraction
Joint entity-relation extraction (JERE) identifies both entities and their relationships simultaneously. Traditional machine-learning based approaches to performing this task require a large corpus of annotated data and lack the ability to easily incorporate domain specific information in the construction of the model. Therefore, creating a model for JERE is often labor intensive, time consuming, and elaboration intolerant. In this paper, we propose harnessing the capabilities of generative pre…

Energy Efficiency in Robotics Software: A Systematic Literature Review (2020-2024)
Aryan Gupta
https://arxiv.org/abs/2508.12170 https://arxiv.org/pdf/2508.…

Energy Efficiency in Robotics Software: A Systematic Literature Review (2020-2024)
This study presents a systematic literature review of software-level approaches to energy efficiency in robotics published from 2020 through 2024, updating and extending pre-2020 evidence. An automated-but-audited pipeline combined Google Scholar seeding, backward/forward snowballing, and large-language-model (LLM) assistance for screening and data extraction, with ~10% human audits at each automated step and consensus-with-tie-breaks for full-text decisions. The final corpus comprises 79 peer-…

Adapting Whisper for Lightweight and Efficient Automatic Speech Recognition of Children for On-device Edge Applications
Satwik Dutta, Shruthigna Chandupatla, John Hansen
https://arxiv.org/abs/2507.14451

Adapting Whisper for Lightweight and Efficient Automatic Speech Recognition of Children for On-device Edge Applications
Reliability on cloud providers for ASR inference to support child-centered voice-based applications is becoming challenging due to regulatory and privacy challenges. Motivated by a privacy-preserving design, this study aims to develop a lightweight & efficient Whisper ASR system capable of running on a Raspberry Pi. Upon evaluation of the MyST corpus and by examining various filtering strategies to fine-tune the `tiny.en' model, a Word Error Rate (WER) of 15.9% was achieved (11.8% filtered). A …

EmoTale: An Enacted Speech-emotion Dataset in Danish
Maja J. Hjuler, Harald V. Skat-R{\o}rdam, Line H. Clemmensen, Sneha Das
https://arxiv.org/abs/2508.14548 https://

EmoTale: An Enacted Speech-emotion Dataset in Danish
While multiple emotional speech corpora exist for commonly spoken languages, there is a lack of functional datasets for smaller (spoken) languages, such as Danish. To our knowledge, Danish Emotional Speech (DES), published in 1997, is the only other database of Danish emotional speech. We present EmoTale; a corpus comprising Danish and English speech recordings with their associated enacted emotion annotations. We demonstrate the validity of the dataset by investigating and presenting its predi…

#DH2025 thanks @flochiff.bsky.social for sharing this link as I wanted to follow up on Pandore! 'Pandore: automating text-processing workflows for humanities researchers' from Sorbonne Université and ObTIC - Observatoire des textes, des idées et des corpus

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

YTCommentVerse: A Multi-Category Multi-Lingual YouTube Comment Corpus
Hridoy Sankar Dutta, Biswadeep Khan
https://arxiv.org/abs/2509.11057 https://arxiv.or…

YTCommentVerse: A Multi-Category Multi-Lingual YouTube Comment Corpus
In this paper, we introduce YTCommentVerse, a large-scale multilingual and multi-category dataset of YouTube comments. It contains over 32 million comments from 178,000 videos contributed by more than 20 million unique users spanning 15 distinct YouTube content categories such as Music, News, Education and Entertainment. Each comment in the dataset includes video and comment IDs, user channel details, upvotes and category labels. With comments in over 50 languages, YTCommentVerse provides a ric…

The Digital Sous Chef -- A Comparative Study on Fine-Tuning Language Models for Recipe Generation
Shubham Pundhir, Ganesh Bagler
https://arxiv.org/abs/2508.14718 https://…

The Digital Sous Chef -- A Comparative Study on Fine-Tuning Language Models for Recipe Generation
We established a rigorous benchmark for text-based recipe generation, a fundamental task in natural language generation. We present a comprehensive comparative study contrasting a fine-tuned GPT-2 large (774M) model against the GPT-2 small (124M) model and traditional LSTM/RNN baselines on the 5-cuisine corpus from RecipeDB. Our key contribution is a targeted tokenization strategy that augments the vocabulary with 23 common fraction tokens and custom structural markers. This approach addresses …

🇺🇦 #NowPlaying on BBCRadio3's #Breakfast
Tenebrae, Wolfgang Amadeus Mozart, The Chamber Orchestra of Europe & Nigel Short:
🎵 Ave verum corpus, K 618
#Tenebrae #WolfgangAmadeusMozart #TheChamberOrchestraofEurope
https://open.spotify.com/track/6nYkXGzsBMInJ5WLrOYT7f

AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design
Zihao Chen, Ji Zhuang, Jinyi Shen, Xiaoyue Ke, Xinyi Yang, Mingjie Zhou, Zhuoyao Du, Xu Yan, Zhouyang Wu, Zhenyu Xu, Jiangli Huang, Li Shang, Xuan Zeng, Fan Yang
https://arxiv.org/abs/2508.10409

AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design
In this paper, we propose AnalogSeeker, an effort toward an open-source foundation language model for analog circuit design, with the aim of integrating domain knowledge and giving design assistance. To overcome the scarcity of data in this field, we employ a corpus collection strategy based on the domain knowledge framework of analog circuits. High-quality, accessible textbooks across relevant subfields are systematically curated and cleaned into a textual domain corpus. To address the complex…

The Expressions of Depression and Anxiety in Chinese Psycho-counseling: Usage of First-person Singular Pronoun and Negative Emotional Words
Lizhi Ma, Tong Zhao, Shuai Zhang, Nirui Song, Hongliang He, Anqi Li, Ran Feng, Huachuan Qiu, Jingsong Ma, Zhenzhong Lan
https://arxiv.org/abs/2507.13839…

The Expressions of Depression and Anxiety in Chinese Psycho-counseling: Usage of First-person Singular Pronoun and Negative Emotional Words
This study explores the relationship between linguistic expressions and psychological states of depression and anxiety within Chinese psycho-counseling interactions, focusing specifically on the usage of first-person singular pronouns and negative emotional words. Utilizing a corpus derived from 735 online counseling sessions, the analysis employed a general linear mixed-effect model to assess linguistic patterns quantified by the Linguistic Inquiry and Word Count (LIWC) software. Results indic…

More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognition
James Tavernor, Emily Mower Provost
https://arxiv.org/abs/2509.12295 https://

More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognition
Speech emotion recognition systems often predict a consensus value generated from the ratings of multiple annotators. However, these models have limited ability to predict the annotation of any one person. Alternatively, models can learn to predict the annotations of all annotators. Adapting such models to new annotators is difficult as new annotators must individually provide sufficient labeled training data. We propose to leverage inter-annotator similarity by using a model pre-trained on a l…

DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning
Weize Liu, Yongchi Zhao, Yijia Luo, Mingyu Xu, Jiaheng Liu, Yanan Li, Xiguo Hu, Yuchi Xu, Wenbo Su, Bo Zheng
https://arxiv.org/abs/2508.12726

DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning
Large language models (LLMs) have achieved remarkable success in many natural language tasks but still struggle with complex, multi-step reasoning, particularly across diverse disciplines. Existing reasoning datasets often either lack disciplinary breadth or the structural depth necessary to elicit robust reasoning behaviors. We propose DESIGNER: a DESIGN-logic-guidEd Reasoning data synthesis pipeline that leverages naturally available, extensive raw documents (book corpus and web corpus) to ge…

Arabic ASR on the SADA Large-Scale Arabic Speech Corpus with Transformer-Based Models
Branislav Gerazov, Marcello Politi, S\'ebastien Brati\`eres
https://arxiv.org/abs/2508.12968

Arabic ASR on the SADA Large-Scale Arabic Speech Corpus with Transformer-Based Models
We explore the performance of several state-of-the-art automatic speech recognition (ASR) models on a large-scale Arabic speech dataset, the SADA (Saudi Audio Dataset for Arabic), which contains 668 hours of high-quality audio from Saudi television shows. The dataset includes multiple dialects and environments, specifically a noisy subset that makes it particularly challenging for ASR. We evaluate the performance of the models on the SADA test set, and we explore the impact of fine-tuning, lang…

MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance
Yi Dong, Yusuke Muraoka, Scott Shi, Yi Zhang
https://arxiv.org/abs/2508.10429 http…

MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance
We present MM-Food-100K, a public 100,000-sample multimodal food intelligence dataset with verifiable provenance. It is a curated approximately 10% open subset of an original 1.2 million, quality-accepted corpus of food images annotated for a wide range of information (such as dish name, region of creation). The corpus was collected over six weeks from over 87,000 contributors using the Codatta contribution model, which combines community sourcing with configurable AI-assisted quality checks; e…

Extracting Document Relations from Search Corpus by Marginalizing over User Queries
Yuki Iwamoto, Kaoru Tsunoda, Ken Kaneiwa
https://arxiv.org/abs/2507.10726

Extracting Document Relations from Search Corpus by Marginalizing over User Queries
Understanding relationships between documents in large-scale corpora is essential for knowledge discovery and information organization. However, existing approaches rely heavily on manual annotation or predefined relationship taxonomies. We propose EDR-MQ (Extracting Document Relations by Marginalizing over User Queries), a novel framework that discovers document relationships through query marginalization. EDR-MQ is based on the insight that strongly related documents often co-occur in results…

AKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes
Zhenwei Dai, Chuan Lei, Asterios Katsifodimos, Xiao Qin, Christos Faloutsos, Huzefa Rangwala
https://arxiv.org/abs/2507.04687

AKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes
How to generate a large, realistic set of tables along with joinability relationships, to stress-test dataset discovery methods? Dataset discovery methods aim to automatically identify related data assets in a data lake. The development and evaluation of such solutions for customers from a wide range of business domains, relies on diverse, high quality and domain-specific tabular benchmarks. Large language models (LLMs) are trained on a wide variety of text data, which can provide a strong foun…

250 years, wars on multiple continents, millions sworn to protect and uphold it, and Mango Zedong decides to Barbara Streisand Effect it by removing key sections.
At least the Seize-her Geezer is bad at being a monstrous dictator.

https://techcrunch.com…

Key sections of the US Constitution deleted from government's website | TechCrunch
Internet sleuths say the U.S. Constitution's website is now missing key sections from its website, including a key legal provision relating to habeas corpus, which protects citizens from unlawful detention.

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, Praveen Rao
https://arxiv.org/abs/2506.19268

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and privacy concerns. Creating HARPT required addressing multiple complexities, such as defining a nuanced label schema, isolating relevant content from large volumes of noisy data, and designing an annotati…

🔍 how many emojis is too many in Petition for Writ of Habeas Corpus form?

Several sections of Article 1 of the U.S. Constitution
appear to have been removed from the official U.S. government website,
as pointed out by sleuths on the internet and as seen by TechCrunch.
These sections largely relate to the powers that Congress has and does not have,
as well as limitations on the powers of individual states.
The removal includes sections relating to habeas corpus,
the powers that protect citizens from unlawful detention. 
S…

Key sections of the US Constitution deleted from government's website | TechCrunch
Internet sleuths say the U.S. Constitution's website is now missing key sections from its website, including a key legal provision relating to habeas corpus, which protects citizens from unlawful detention.

MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection
Paulo Mendes (GECAD, ISEP, Polytechnic of Porto, Portugal), Eva Maia (GECAD, ISEP, Polytechnic of Porto, Portugal), Isabel Pra\c{c}a (GECAD, ISEP, Polytechnic of Porto, Portugal)
https://arxiv.org/abs/2507.17978

MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection
Phishing emails continue to pose a significant threat to cybersecurity by exploiting human vulnerabilities through deceptive content and malicious payloads. While Machine Learning (ML) models are effective at detecting phishing threats, their performance largely relies on the quality and diversity of the training data. This paper presents MeAJOR (Merged email Assets from Joint Open-source Repositories) Corpus, a novel, multi-source phishing email dataset designed to overcome critical limitation…

Grammatical Structure and Grammatical Variations in Non-Metric Iranian Classical Music
Maziar Kanani, Sean O Leary, James McDermott
https://arxiv.org/abs/2507.10708

Grammatical Structure and Grammatical Variations in Non-Metric Iranian Classical Music
In this study we introduce a symbolic dataset composed of non-metric Iranian classical music, and algorithms for structural parsing of this music, and generation of variations. The corpus comprises MIDI files and data sheets of Dastgah Shour from Radif Mirza Abdollah, the foundational repertoire of Iranian classical music. Furthermore, we apply our previously-introduced algorithm for parsing melodic structure (Kanani et al., 2023b)to the dataset. Unlike much Western music, this type of non-metr…

Health Insurance Coverage Rule Interpretation Corpus: Law, Policy, and Medical Guidance for Health Insurance Coverage Understanding
Mike Gartner
https://arxiv.org/abs/2508.03718

Health Insurance Coverage Rule Interpretation Corpus: Law, Policy, and Medical Guidance for Health Insurance Coverage Understanding
U.S. health insurance is complex, and inadequate understanding and limited access to justice have dire implications for the most vulnerable. Advances in natural language processing present an opportunity to support efficient, case-specific understanding, and to improve access to justice and healthcare. Yet existing corpora lack context necessary for assessing even simple cases. We collect and release a corpus of reputable legal and medical text related to U.S. health insurance. We also introduc…

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Annotating Satellite Images of Forests with Keywords from a Specialized Corpus in the Context of ...
Nathalie Neptune, Josiane Mothe

Key sections of the US Constitution deleted from government’s website;
Changes in Article 1 of the U.S. Constitution: Large parts of Section 8 have been removed, and Sections 9 and 10 have been deleted altogether.
https://techcrunch.com/2025/08/06/key-

Key sections of the US Constitution deleted from government's website | TechCrunch
Internet sleuths say the U.S. Constitution's website is now missing key sections from its website, including a key legal provision relating to habeas corpus, which protects citizens from unlawful detention.

The MSP-Podcast Corpus
Carlos Busso, Reza Lotfian, Kusha Sridhar, Ali N. Salman, Wei-Cheng Lin, Lucas Goncalves, Srinivas Parthasarathy, Abinay Reddy Naini, Seong-Gyun Leem, Luz Martinez-Lucas, Huang-Cheng Chou, Pravin Mote
https://arxiv.org/abs/2509.09791

The MSP-Podcast Corpus
The availability of large, high-quality emotional speech databases is essential for advancing speech emotion recognition (SER) in real-world scenarios. However, many existing databases face limitations in size, emotional balance, and speaker diversity. This study describes the MSP-Podcast corpus, summarizing our ten-year effort. The corpus consists of over 400 hours of diverse audio samples from various audio-sharing websites, all of which have Common Licenses that permit the distribution of th…

Overcoming Low-Resource Barriers in Tulu: Neural Models and Corpus Creation for OffensiveLanguage Identification
Anusha M D, Deepthi Vikram, Bharathi Raja Chakravarthi, Parameshwar R Hegde
https://arxiv.org/abs/2508.11166

Overcoming Low-Resource Barriers in Tulu: Neural Models and Corpus Creation for OffensiveLanguage Identification
Tulu, a low-resource Dravidian language predominantly spoken in southern India, has limited computational resources despite its growing digital presence. This study presents the first benchmark dataset for Offensive Language Identification (OLI) in code-mixed Tulu social media content, collected from YouTube comments across various domains. The dataset, annotated with high inter-annotator agreement (Krippendorff's alpha = 0.984), includes 3,845 comments categorized into four classes: Not Offens…

Even outside the digital transformation of "planning", I'm very interested (as a member of the public) in the development of a rich corpus of public geospatial data. (Some deep unexpressed cartographic instinct?) This blogpost is an update on the status of that:
https://…

Standardising the planning application process: Progress and next steps – MHCLG Digital
The blog of the digital and data teams in MHCLG

It's interesting how much Wikipedia's list of LLM catchphrases comes straight out of academic writing jargon.
Probably as a lot of the corpus that was mined for the LLM training goes back to the academic literature thanks to Sci-Hub et al.
https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup/AI_catchphrases#Language_and_tone

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI

Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding
Zhifeng Kong, Arushi Goel, Joao Felipe Santos, Sreyan Ghosh, Rafael Valle, Wei Ping, Bryan Catanzaro
https://arxiv.org/abs/2508.11818

Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding
Chain-of-thought reasoning has demonstrated significant improvements in large language models and vision language models, yet its potential for audio language models remains largely unexplored. In this technical report, we take a preliminary step towards closing this gap. For better assessment of sound reasoning, we propose AF-Reasoning-Eval, a benchmark targeting common-sense reasoning and the ability to discriminate among closely related choices. To prepare training corpus for sound reasoning…

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Storage places in diplomatic texts (7th-13th centuries). Lexical, semantic, and digital investigation
Nicolas Perreaux (LAMOP, CNRS)
https://arxiv.org/abs/2509.12230 https://

Storage places in diplomatic texts (7th-13th centuries). Lexical, semantic, and digital investigation
This study examines the evolution of references to grain storage structures in medieval European charters, based on a quantitative and semantic analysis of the digitized CEMA (Cartae Europae Medii Aevi) corpus comprising more than 225,000 documents. The author applies text mining and distributional analysis methods to a lexicon of some forty terms designating storage locations (grangia, horreum, granarium, granica, etc.), cross-referencing these data with references to grain and analyzing their…

Podcasts as a Medium for Participation in Collective Action: A Case Study of Black Lives Matter
Theodora Moldovan, Arianna Pera, Davide Vega, Luca Maria Aiello
https://arxiv.org/abs/2509.13197

Podcasts as a Medium for Participation in Collective Action: A Case Study of Black Lives Matter
We study how participation in collective action is articulated in podcast discussions, using the Black Lives Matter (BLM) movement as a case study. While research on collective action discourse has primarily focused on text-based content, this study takes a first step toward analyzing audio formats by using podcast transcripts. Using the Structured Podcast Research Corpus (SPoRC), we investigated spoken language expressions of participation in collective action, categorized as problem-solution,…

Patent Language Model Pretraining with ModernBERT
Amirhossein Yousefiramandi, Ciaran Cooney
https://arxiv.org/abs/2509.14926 https://arxiv.org/pdf/2509.149…

Patent Language Model Pretraining with ModernBERT
Transformer-based language models such as BERT have become foundational in NLP, yet their performance degrades in specialized domains like patents, which contain long, technical, and legally structured text. Prior approaches to patent NLP have primarily relied on fine-tuning general-purpose models or domain-adapted variants pretrained with limited data. In this work, we pretrain 3 domain-specific masked language models for patents, using the ModernBERT architecture and a curated corpus of over …

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation
Arnab Kumar Roy, Hemant Kumar Kathania, Paban Sapkota
https://arxiv.org/abs/2509.13442

Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation
Dysarthric speech severity classification is crucial for objective clinical assessment and progress monitoring in individuals with motor speech disorders. Although prior methods have addressed this task, achieving robust generalization in speaker-independent (SID) scenarios remains challenging. This work introduces DSSCNet, a novel deep neural architecture that combines Convolutional, Squeeze-Excitation (SE), and Residual network, helping it extract discriminative representations of dysarthric …

AI Answer Engine Citation Behavior An Empirical Analysis of the GEO16 Framework
Arlen Kumar, Leanid Palkhouski
https://arxiv.org/abs/2509.10762 https://arx…

AI Answer Engine Citation Behavior An Empirical Analysis of the GEO16 Framework
AI answer engines increasingly mediate access to domain knowledge by generating responses and citing web sources. We introduce GEO-16, a 16 pillar auditing framework that converts on page quality signals into banded pillar scores and a normalized GEO score G that ranges from 0 to 1. Using 70 product intent prompts, we collected 1,702 citations across three engines (Brave Summary, Google AI Overviews, and Perplexity) and audited 1,100 unique URLs. In our corpus, the engines differed in the GEO q…

Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval
Amanda Chan, James Jiayu Liu, He Kai, Onno P. Kampman
https://arxiv.org/abs/2509.13626 …

Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval
Access to reliable mental health information is vital for early help-seeking, yet expanding knowledge bases is resource-intensive and often misaligned with user needs. This results in poor performance of retrieval systems when presented concerns are not covered or expressed in informal or contextualized language. We present an AI-based gap-informed framework for corpus augmentation that authentically identifies underrepresented topics (gaps) by overlaying naturalistic user data such as forum po…

Over the last three years, the head of a small charter school network that serves fewer than 1,000 students has taken home up to $870,000 annually,
a startling amount that appears to be the highest for any public school superintendent in Texas and among the top in the nation.
Valere Public Schools Superintendent Salvador Cavazos’ compensation to run three campuses in Austin, Corpus Christi and Brownsville
exceeds the less than $450,000 that New York City’s chancellor makes …

This charter school superintendent makes $870,000. He leads a district with 1,000 students.
On paper, Salvador Cavazos earns less than $300,000 to run Valere Public Schools, a small Texas charter network. But taxpayers likely aren’t aware that his total pay makes him one of the country’s highest-earning superintendents.

Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki, Hiroshi Saruwatari
https://arxiv.org/abs/2507.08319

Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
The construction of high-quality datasets is a cornerstone of modern text-to-speech (TTS) systems. However, the increasing scale of available data poses significant challenges, including storage constraints. To address these issues, we propose a TTS corpus construction method based on active learning. Unlike traditional feed-forward and model-agnostic corpus construction approaches, our method iteratively alternates between data collection and model training, thereby focusing on acquiring data …

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Automated Classification of Tutors' Dialogue Acts Using Generative AI: A Case Study Using the CIMA Corpus
Liqun He, Jiaqi Xu
https://arxiv.org/abs/2509.09125 https://…

Automated Classification of Tutors' Dialogue Acts Using Generative AI: A Case Study Using the CIMA Corpus
This study explores the use of generative AI for automating the classification of tutors' Dialogue Acts (DAs), aiming to reduce the time and effort required by traditional manual coding. This case study uses the open-source CIMA corpus, in which tutors' responses are pre-annotated into four DA categories. Both GPT-3.5-turbo and GPT-4 models were tested using tailored prompts. Results show that GPT-4 achieved 80% accuracy, a weighted F1-score of 0.81, and a Cohen's Kappa of 0.74, surpassing base…

Tokenization Strategies for Low-Resource Agglutinative Languages in Word2Vec: Case Study on Turkish and Finnish
Jinfan Frank Hu
https://arxiv.org/abs/2509.14238 https://

Tokenization Strategies for Low-Resource Agglutinative Languages in Word2Vec: Case Study on Turkish and Finnish
Tokenization plays a critical role in processing agglutinative languages, where a single word can encode multiple morphemes carrying syntactic and semantic information. This study evaluates the impact of various tokenization strategies - word-level, character-level, n-gram, and Byte Pair Encoding (BPE) - on the quality of static word embeddings generated by Word2Vec for Turkish and Finnish. Using a 10,000-article Wikipedia corpus, we trained models under low-resource conditions and evaluated th…

Beyond Bars: Distribution of Edit Operations in Historical Prints
Adrian Nachtwey, Fabian C. Moss, Anna Viktoria Katrin Plaksin
https://arxiv.org/abs/2509.12786 https://

Beyond Bars: Distribution of Edit Operations in Historical Prints
In this paper, we present a method for conducting comparative corpus studies in musicology that reduces the time-consuming digitization process. Instead of encoding whole corpora of musical sources, we suggest sampling bars from these sources. We address the challenge of selecting representative samples and evaluate three different sampling methods. We used Beethoven's Bagatelles Op. 33 as a case study to find the method that works best in finding samples representative with respect to differen…

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Context-Aware Search and Retrieval Over Erasure Channels
Sara Ghasvarianjahromi, Yauhen Yakimenka, J\"org Kliewer
https://arxiv.org/abs/2507.11894 htt…

Context-Aware Search and Retrieval Over Erasure Channels
This paper introduces and analyzes a search and retrieval model that adopts key semantic communication principles from retrieval-augmented generation. We specifically present an information-theoretic analysis of a remote document retrieval system operating over a symbol erasure channel. The proposed model encodes the feature vector of a query, derived from term-frequency weights of a language corpus by using a repetition code with an adaptive rate dependent on the contextual importance of the t…

Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, Bernard Ghanem
https://arxiv.org/abs/2509.14008

Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong AR$\leftrightarrow$EN teacher to FP8 (yielding $\sim$2$\times$ higher throughput with no quality loss) and use it to create high-fidelity bilingual supervision. A lightweight language model LFM2-1.2B is then fine-tuned on this data and used to translate high-quality English instruction sets into Arabic, producing a million-scale corpus tailored to…

A World in Print: Introducing a Danish-Norwegian corpus of historical newspapers
Johan Heinsen, Camilla B{\o}geskov
https://arxiv.org/abs/2509.02356 https://

A World in Print: Introducing a Danish-Norwegian corpus of historical newspapers
This Data Descriptor introduces the dataset Enevaeldens Nyheder Online (News during Absolutism Online). The Enevaeldens Nyheder Online (ENO) dataset provides a reconstruction of the contents of major newspapers in Denmark and Norway during the period of Absolutism (1660-1849). The dataset contains approx. 474 million words, created using neural networks designed to process digitised microfilm versions of Danish newspapers as well as a smaller selection of Norwegian publications that were all hi…

About 9 million years ago, a natural inbreeding in the wild between tomato plants and a potato-like plant species in present-day South America gave way to what we know as the potato.
This new (and nutritious) plant arose from an evolutionary event that triggered the formation of the tuber–the underground structure that plants like potatoes, yams, and taros use to store food.
The findings are detailed in a study published July 31 in the journal Cell.

How the tomato created the potato
The starchy staple evolved from the tomato about 9 million years ago.

ParCzech4Speech: A New Speech Corpus Derived from Czech Parliamentary Data
Vladislav Stankov, Maty\'a\v{s} Kopp, Ond\v{r}ej Bojar
https://arxiv.org/abs/2509.06675 https://…

ParCzech4Speech: A New Speech Corpus Derived from Czech Parliamentary Data
We introduce ParCzech4Speech 1.0, a processed version of the ParCzech 4.0 corpus, targeted at speech modeling tasks with the largest variant containing 2,695 hours. We combined the sound recordings of the Czech parliamentary speeches with the official transcripts. The recordings were processed with WhisperX and Wav2Vec 2.0 to extract automated audio-text alignment. Our processing pipeline improves upon the ParCzech 3.0 speech recognition version by extracting more data with higher alignment rel…

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

Revealing the Hidden Temporal Structure of HubertSoft Embeddings based on the Russian Phonetic Corpus
Anastasia Ananeva, Anton Tomilov, Marina Volkova
https://arxiv.org/abs/2507.06794

Revealing the Hidden Temporal Structure of HubertSoft Embeddings based on the Russian Phonetic Corpus
Self-supervised learning (SSL) models such as Wav2Vec 2.0 and HuBERT have shown remarkable success in extracting phonetic information from raw audio without labelled data. While prior work has demonstrated that SSL embeddings encode phonetic features at the frame level, it remains unclear whether these models preserve temporal structure, specifically, whether embeddings at phoneme boundaries reflect the identity and order of adjacent phonemes. This study investigates the extent to which boundar…

email_enron: Email network (Enron corpus)
The Enron email corpus, containing all the email communication from the Enron corporation, which was made public as a result of legal action. Nodes are email addresses and node i links to node j if i sent at least one email to address j. Non-Enron email addresses are also present, but only their links to/from Enron addresses are observed.
This network has 36692 nodes and 367662 edges.
Tags: Social, Communication, Unweighted, Multigr…

IdolSongsJp Corpus: A Multi-Singer Song Corpus in the Style of Japanese Idol Groups
Hitoshi Suda, Junya Koguchi, Shunsuke Yoshida, Tomohiko Nakamura, Satoru Fukayama, Jun Ogata
https://arxiv.org/abs/2507.01349

IdolSongsJp Corpus: A Multi-Singer Song Corpus in the Style of Japanese Idol Groups
Japanese idol groups, comprising performers known as "idols," are an indispensable part of Japanese pop culture. They frequently appear in live concerts and television programs, entertaining audiences with their singing and dancing. Similar to other J-pop songs, idol group music covers a wide range of styles, with various types of chord progressions and instrumental arrangements. These tracks often feature numerous instruments and employ complex mastering techniques, resulting in high signal lo…

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
Chenye Zou, Xingyue Wen, Tianyi Hu, Qian Janice Wang, Daniel Hershcovich
https://arxiv.org/abs/2509.12961

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
Recent advances in large language models (LLMs) have opened the door to culture-aware language tasks. We introduce the novel problem of adapting wine reviews across Chinese and English, which goes beyond literal translation by incorporating regional taste preferences and culture-specific flavor descriptors. In a case study on cross-cultural wine review adaptation, we compile the first parallel corpus of professional reviews, containing 8k Chinese and 16k Anglophone reviews. We benchmark both ne…

GeoGPT.RAG Technical Report
Fei Huang, Fan Wu, Zeqing Zhang, Qihao Wang, Long Zhang, Grant Michael Boquet, Hongyang Chen
https://arxiv.org/abs/2509.09686 https://

GeoGPT.RAG Technical Report
GeoGPT is an open large language model system built to advance research in the geosciences. To enhance its domain-specific capabilities, we integrated Retrieval Augmented Generation(RAG), which augments model outputs with relevant information retrieved from an external knowledge source. GeoGPT uses RAG to draw from the GeoGPT Library, a specialized corpus curated for geoscientific content, enabling it to generate accurate, context-specific answers. Users can also create personalized knowledge b…

StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features
Jeremi K. Ochab, Mateusz Matias, Tymoteusz Boba, Tomasz Walkowiak
https://arxiv.org/abs/2507.12064

StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features
This submission to the binary AI detection task is based on a modular stylometric pipeline, where: public spaCy models are used for text preprocessing (including tokenisation, named entity recognition, dependency parsing, part-of-speech tagging, and morphology annotation) and extracting several thousand features (frequencies of n-grams of the above linguistic annotations); light-gradient boosting machines are used as the classifier. We collect a large corpus of more than 500 000 machine-generat…

SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN
Luca Mariotti, Veronica Guidetti, Federica Mandreoli
https://arxiv.org/abs/2507.06895

SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN
The growing demand for efficient knowledge graph (KG) enrichment leveraging external corpora has intensified interest in relation extraction (RE), particularly under low-supervision settings. To address the need for adaptable and noise-resilient RE solutions that integrate seamlessly with pre-trained large language models (PLMs), we introduce SCoRE, a modular and cost-effective sentence-level RE system. SCoRE enables easy PLM switching, requires no finetuning, and adapts smoothly to diverse cor…

WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation
Longhao Li, Zhao Guo, Hongjie Chen, Yuhang Dai, Ziyu Zhang, Hongfei Xue, Tianlun Zuo, Chengyou Wang, Shuiyuan Wang, Jie Li, Xin Xu, Hui Bu, Binbin Zhang, Ruibin Yuan, Ziya Zhou, Wei Xue, Lei Xie
https://arxiv.org/abs/2509.03959

WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation
The development of speech understanding and generation has been significantly accelerated by the availability of large-scale, high-quality speech datasets. Among these, ASR and TTS are regarded as the most established and fundamental tasks. However, for Cantonese (Yue Chinese), spoken by approximately 84.9 million native speakers worldwide, limited annotated resources have hindered progress and resulted in suboptimal ASR and TTS performance. To address this challenge, we propose WenetSpeech-Pip…

MDC-R: The Minecraft Dialogue Corpus with Reference
Chris Madge, Maris Camilleri, Paloma Carretero Garcia, Mladen Karan, Juexi Shao, Prashant Jayannavar, Julian Hough, Benjamin Roth, Massimo Poesio
https://arxiv.org/abs/2506.22062

MDC-R: The Minecraft Dialogue Corpus with Reference
We introduce the Minecraft Dialogue Corpus with Reference (MDC-R). MDC-R is a new language resource that supplements the original Minecraft Dialogue Corpus (MDC) with expert annotations of anaphoric and deictic reference. MDC's task-orientated, multi-turn, situated dialogue in a dynamic environment has motivated multiple annotation efforts, owing to the interesting linguistic phenomena that this setting gives rise to. We believe it can serve as a valuable resource when annotated with reference,…

The IRMA Dataset: A Structured Audio-MIDI Corpus for Iranian Classical Music
Sepideh Shafiei, Shapour Hakam
https://arxiv.org/abs/2508.19876 https://arxiv.…

The IRMA Dataset: A Structured Audio-MIDI Corpus for Iranian Classical Music
We present the IRMA Dataset (Iranian Radif MIDI Audio), a multi-level, open-access corpus designed for the computational study of Iranian classical music, with a particular emphasis on the radif, a structured repertoire of modal-melodic units central to pedagogy and performance. The dataset combines symbolic MIDI representations, phrase-level audio-MIDI alignment, musicological transcriptions in PDF format, and comparative tables of theoretical information curated from a range of performers and…

HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches
Jiejun Tan, Zhicheng Dou, Yan Yu, Jiehan Cheng, Qiang Ju, Jian Xie, Ji-Rong Wen
https://arxiv.org/abs/2508.08088

HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches
Recently, large reasoning models have demonstrated strong mathematical and coding abilities, and deep search leverages their reasoning capabilities in challenging information retrieval tasks. Existing deep search works are generally limited to a single knowledge source, either local or the Web. However, enterprises often require private deep search systems that can leverage search tools over both local and the Web corpus. Simply training an agent equipped with multiple search tools using flat r…

Building High-Quality Datasets for Portuguese LLMs: From Common Crawl Snapshots to Industrial-Grade Corpora
Thales Sales Almeida, Rodrigo Nogueira, Helio Pedrini
https://arxiv.org/abs/2509.08824

Building High-Quality Datasets for Portuguese LLMs: From Common Crawl Snapshots to Industrial-Grade Corpora
The performance of large language models (LLMs) is deeply influenced by the quality and composition of their training data. While much of the existing work has centered on English, there remains a gap in understanding how to construct effective training corpora for other languages. We explore scalable methods for building web-based corpora for LLMs. We apply them to build a new 120B token corpus in Portuguese that achieves competitive results to an industrial-grade corpus. Using a continual pre…

JIS: A Speech Corpus of Japanese Idol Speakers with Various Speaking Styles
Yuto Kondo, Hirokazu Kameoka, Kou Tanaka, Takuhiro Kaneko
https://arxiv.org/abs/2506.18296

JIS: A Speech Corpus of Japanese Idol Speakers with Various Speaking Styles
We construct Japanese Idol Speech Corpus (JIS) to advance research in speech generation AI, including text-to-speech synthesis (TTS) and voice conversion (VC). JIS will facilitate more rigorous evaluations of speaker similarity in TTS and VC systems since all speakers in JIS belong to a highly specific category: "young female live idols" in Japan, and each speaker is identified by a stage name, enabling researchers to recruit listeners familiar with these idols for listening experiments. With i…

A Computational Approach to Analyzing Language Change and Variation in the Constructed Language Toki Pona
Daniel Huang, Hyoun-A Joo
https://arxiv.org/abs/2508.10246 https://

A Computational Approach to Analyzing Language Change and Variation in the Constructed Language Toki Pona
This study explores language change and variation in Toki Pona, a constructed language with approximately 120 core words. Taking a computational and corpus-based approach, the study examines features including fluid word classes and transitivity in order to examine (1) changes in preferences of content words for different syntactic positions over time and (2) variation in usage across different corpora. The results suggest that sociolinguistic factors influence Toki Pona in the same way as natu…

The Mediomatix Corpus: Parallel Data for Romansh Idioms via Comparable Schoolbooks
Zachary Hopton, Jannis Vamvas, Andrin B\"uchler, Anna Rutkiewicz, Rico Cathomas, Rico Sennrich
https://arxiv.org/abs/2508.16371

The Mediomatix Corpus: Parallel Data for Romansh Idioms via Comparable Schoolbooks
The five idioms (i.e., varieties) of the Romansh language are largely standardized and are taught in the schools of the respective communities in Switzerland. In this paper, we present the first parallel corpus of Romansh idioms. The corpus is based on 291 schoolbook volumes, which are comparable in content for the five idioms. We use automatic alignment methods to extract 207k multi-parallel segments from the books, with more than 2M tokens in total. A small-scale human evaluation confirms tha…

JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus
Masaaki Nagata, Katsuki Chousa, Norihito Yasuda
https://arxiv.org/abs/2508.16303 https://

JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus
We constructed JaParaPat (Japanese-English Parallel Patent Application Corpus), a bilingual corpus of more than 300 million Japanese-English sentence pairs from patent applications published in Japan and the United States from 2000 to 2021. We obtained the publication of unexamined patent applications from the Japan Patent Office (JPO) and the United States Patent and Trademark Office (USPTO). We also obtained patent family information from the DOCDB, that is a bibliographic database maintained…

Prominence-aware automatic speech recognition for conversational speech
Julian Linke, Barbara Schuppler
https://arxiv.org/abs/2509.10116 https://arxiv.org/…

Prominence-aware automatic speech recognition for conversational speech
This paper investigates prominence-aware automatic speech recognition (ASR) by combining prominence detection and speech recognition for conversational Austrian German. First, prominence detectors were developed by fine-tuning wav2vec2 models to classify word-level prominence. The detector was then used to automatically annotate prosodic prominence in a large corpus. Based on those annotations, we trained novel prominence-aware ASR systems that simultaneously transcribe words and their prominen…

Benchmark of stylistic variation in LLM-generated texts
Ji\v{r}\'i Mili\v{c}ka, Anna Marklov\'a, V\'aclav Cvr\v{c}ek
https://arxiv.org/abs/2509.10179 https://…

Benchmark of stylistic variation in LLM-generated texts
This study investigates the register variation in texts written by humans and comparable texts produced by large language models (LLMs). Biber's multidimensional analysis (MDA) is applied to a sample of human-written texts and AI-created texts generated to be their counterparts to find the dimensions of variation in which LLMs differ most significantly and most systematically from humans. As textual material, a new LLM-generated corpus AI-Brown is used, which is comparable to BE-21 (a Brown fam…

Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages
Nasma Chaoui, Richard Khoury
https://arxiv.org/abs/2508.10683 https://

Neural Machine Translation for Coptic-French: Strategies for Low-Resource Ancient Languages
This paper presents the first systematic study of strategies for translating Coptic into French. Our comprehensive pipeline systematically evaluates: pivot versus direct translation, the impact of pre-training, the benefits of multi-version fine-tuning, and model robustness to noise. Utilizing aligned biblical corpora, we demonstrate that fine-tuning with a stylistically-varied and noise-aware training corpus significantly enhances translation quality. Our findings provide crucial practical ins…

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records
Abdulrahman Allam, Seif Ahmed, Ali Hamdi, Khaled Shaban
https://arxiv.org/abs/2509.10108

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records
The development of medical chatbots in Arabic is significantly constrained by the scarcity of large-scale, high-quality annotated datasets. While prior efforts compiled a dataset of 20,000 Arabic patient-doctor interactions from social media to fine-tune large language models (LLMs), model scalability and generalization remained limited. In this study, we propose a scalable synthetic data augmentation strategy to expand the training corpus to 100,000 records. Using advanced generative AI system…

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
Mohamed Basem, Mohamed Younes, Seif Ahmed, Abdelrahman Moustafa
https://arxiv.org/abs/2509.10040

!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
We present MSAs winning system for the BAREC 2025 Shared Task on fine-grained Arabic readability assessment, achieving first place in six of six tracks. Our approach is a confidence-weighted ensemble of four complementary transformer models (AraBERTv2, AraELECTRA, MARBERT, and CAMeLBERT) each fine-tuned with distinct loss functions to capture diverse readability signals. To tackle severe class imbalance and data scarcity, we applied weighted training, advanced preprocessing, SAMER corpus relabe…

DACTYL: Diverse Adversarial Corpus of Texts Yielded from Large Language Models
Shantanu Thorat, Andrew Caines
https://arxiv.org/abs/2508.00619 https://arxi…

DACTYL: Diverse Adversarial Corpus of Texts Yielded from Large Language Models
Existing AIG (AI-generated) text detectors struggle in real-world settings despite succeeding in internal testing, suggesting that they may not be robust enough. We rigorously examine the machine-learning procedure to build these detectors to address this. Most current AIG text detection datasets focus on zero-shot generations, but little work has been done on few-shot or one-shot generations, where LLMs are given human texts as an example. In response, we introduce the Diverse Adversarial Corp…

DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures
Benno Uthayasooriyar, Antoine Ly, Franck Vermet, Caio Corro
https://arxiv.org/abs/2507.08606

DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures
We introduce DocPolarBERT, a layout-aware BERT model for document understanding that eliminates the need for absolute 2D positional embeddings. We extend self-attention to take into account text block positions in relative polar coordinate system rather than the Cartesian one. Despite being pre-trained on a dataset more than six times smaller than the widely used IIT-CDIP corpus, DocPolarBERT achieves state-of-the-art results. These results demonstrate that a carefully designed attention mechan…

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Bruno Alexandre Rosa, Hil\'ario Oliveira, Luiz Rodrigues, Eduardo Araujo Oliveira, Rafael Ferreira Mello
https://arxiv.org/abs/2507.08487

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Essays are considered a valuable mechanism for evaluating learning outcomes in writing. Textual cohesion is an essential characteristic of a text, as it facilitates the establishment of meaning between its parts. Automatically scoring cohesion in essays presents a challenge in the field of educational artificial intelligence. The machine learning algorithms used to evaluate texts generally do not consider the individual characteristics of the instances that comprise the analysed corpus. In this…

Speech-Based Depressive Mood Detection in the Presence of Multiple Sclerosis: A Cross-Corpus and Cross-Lingual Study
Monica Gonzalez-Machorro, Uwe Reichel, Pascal Hecker, Helly Hammer, Hesam Sagha, Florian Eyben, Robert Hoepner, Bj\"orn W. Schuller
https://arxiv.org/abs/2508.18092

Speech-Based Depressive Mood Detection in the Presence of Multiple Sclerosis: A Cross-Corpus and Cross-Lingual Study
Depression commonly co-occurs with neurodegenerative disorders like Multiple Sclerosis (MS), yet the potential of speech-based Artificial Intelligence for detecting depression in such contexts remains unexplored. This study examines the transferability of speech-based depression detection methods to people with MS (pwMS) through cross-corpus and cross-lingual analysis using English data from the general population and German data from pwMS. Our approach implements supervised machine learning mo…

SinLlama - A Large Language Model for Sinhala
H. W. K. Aravinda, Rashad Sirajudeen, Samith Karunathilake, Nisansa de Silva, Surangika Ranathunga, Rishemjit Kaur
https://arxiv.org/abs/2508.09115

SinLlama - A Large Language Model for Sinhala
Low-resource languages such as Sinhala are often overlooked by open-source Large Language Models (LLMs). In this research, we extend an existing multilingual LLM (Llama-3-8B) to better serve Sinhala. We enhance the LLM tokenizer with Sinhala specific vocabulary and perform continual pre-training on a cleaned 10 million Sinhala corpus, resulting in the SinLlama model. This is the very first decoder-based open-source LLM with explicit Sinhala support. When SinLlama was instruction fine-tuned for …

OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages
Rapha\"el Merx, Hanna Suominen, Trevor Cohn, Ekaterina Vylomova
https://arxiv.org/abs/2508.16048

OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages
In machine translation (MT), health is a high-stakes domain characterised by widespread deployment and domain-specific vocabulary. However, there is a lack of MT evaluation datasets for low-resource languages in this domain. To address this gap, we introduce OpenWHO, a document-level parallel corpus of 2,978 documents and 26,824 sentences from the World Health Organization's e-learning platform. Sourced from expert-authored, professionally translated materials shielded from web-crawling, OpenWH…

ViRanker: A BGE-M3 & Blockwise Parallel Transformer Cross-Encoder for Vietnamese Reranking
Phuong-Nam Dang, Kieu-Linh Nguyen, Thanh-Hieu Pham
https://arxiv.org/abs/2509.09131

ViRanker: A BGE-M3 & Blockwise Parallel Transformer Cross-Encoder for Vietnamese Reranking
This paper presents ViRanker, a cross-encoder reranking model tailored to the Vietnamese language. Built on the BGE-M3 encoder and enhanced with the Blockwise Parallel Transformer, ViRanker addresses the lack of competitive rerankers for Vietnamese, a low-resource language with complex syntax and diacritics. The model was trained on an 8 GB curated corpus and fine-tuned with hybrid hard-negative sampling to strengthen robustness. Evaluated on the MMARCO-VI benchmark, ViRanker achieves strong ea…

A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
Oleg Silcenco, Marcos R. Machad, Wallace C. Ugulino, Daniel Braun
https://arxiv.org/abs/2508.17994

A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
Aspect-based sentiment analysis enhances sentiment detection by associating it with specific aspects, offering deeper insights than traditional sentiment analysis. This study introduces a manually annotated dataset of 10,814 multilingual customer reviews covering brick-and-mortar retail stores, labeled with eight aspect categories and their sentiment. Using this dataset, the performance of GPT-4 and LLaMA-3 in aspect based sentiment analysis is evaluated to establish a baseline for the newly in…

LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge
Dima Galat, Diego Molla-Aliod
https://arxiv.org/abs/2509.08596 https://

LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge
Biomedical question answering (QA) poses significant challenges due to the need for precise interpretation of specialized knowledge drawn from a vast, complex, and rapidly evolving corpus. In this work, we explore how large language models (LLMs) can be used for information retrieval (IR), and an ensemble of zero-shot models can accomplish state-of-the-art performance on a domain-specific Yes/No QA task. Evaluating our approach on the BioASQ challenge tasks, we show that ensembles can outperfor…

AIxcellent Vibes at GermEval 2025 Shared Task on Candy Speech Detection: Improving Model Performance by Span-Level Training
Christian Rene Thelen, Patrick Gustav Blaneck, Tobias Bornheim, Niklas Grieger, Stephan Bialonski
https://arxiv.org/abs/2509.07459

AIxcellent Vibes at GermEval 2025 Shared Task on Candy Speech Detection: Improving Model Performance by Span-Level Training
Positive, supportive online communication in social media (candy speech) has the potential to foster civility, yet automated detection of such language remains underexplored, limiting systematic analysis of its impact. We investigate how candy speech can be reliably detected in a 46k-comment German YouTube corpus by monolingual and multilingual language models, including GBERT, Qwen3 Embedding, and XLM-RoBERTa. We find that a multilingual XLM-RoBERTa-Large model trained to detect candy speech a…

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams
Matthew Anderson Hendricks, Alice Cicirello
https://arxiv.org/abs/2507.06803

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams
This paper contributes to speeding up the design and deployment of engineering dynamical systems by proposing a strategy for exploiting domain and expert knowledge for the automated generation of dynamical system computational model starting from a corpus of document relevant to the dynamical system of interest and an input document describing the specific system. This strategy is implemented in five steps and, crucially, it uses system modeling language diagrams (SysML) to extract accurate inf…

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Amal Chebbi, Babajide Kolade
https://arxiv.org/abs/2509.07177 https://arxiv.org…

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Large Language Models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their effectiveness in specialized fields such as energy, where deep technical expertise and precise domain knowledge are essential. In this paper, we introduce EnergyGPT, a domain-specialized language model tailored for the energy sector, developed by fine-tuning LLaMA 3.1-8B model using Supervised Fine-Tuning on a high-quality, curated corpus of energy-rel…

SIGIR 2025 -- LiveRAG Challenge Report
David Carmel, Simone Filice, Guy Horowitz, Yoelle Maarek, Oren Somekh, Ran Tavory
https://arxiv.org/abs/2507.04942 h…

SIGIR 2025 -- LiveRAG Challenge Report
The LiveRAG Challenge at SIGIR 2025, held between March and May 2025, provided a competitive platform for advancing Retrieval-Augmented Generation (RAG) technologies. Participants from academia and industry were invited to develop a RAG-based question-answering system using a fixed corpus (Fineweb-10BT) and a common open-source LLM (Falcon3-10B-Instruct). The goal was to facilitate challenging comparisons of retrieval and prompting strategies. During the Live Challenge Day, 70 teams from 27 dif…

Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Kai-Robin Lange, Tobias Schmidt, Matthias Reccius, Henrik M\"uller, Michael Roos, Carsten Jentsch
https://arxiv.org/abs/2506.20269

Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction methods such as Large Language Models do well in capturing typical narrative elements or even the complex structure of a narrative, applying them to an entire corpus comes with obstacles, such as a high financial or computational cost. We propose a combination of the language understa…