Tootfinder

Looking forward to reading this! “Making BERT Feel at Home. Modelling Domestic Space in 19th-Century British and Irish Fiction”, Journal of Computational Literary Studies4(1). doi: https://doi.org/10.48694/jcls.4164
By Guhr, S., Monaco, J., Sherman, A., Warner, M. & Algee-Hewitt, M

Making BERT Feel at Home. Modelling Domestic Space in 19th-Century British and Irish Fiction
We introduce a novel approach to detecting domestic space in literary texts beyond explicit spatial markers like “home” or “house.” Using a pre-trained English BERT model fine-tuned on manually annotated passages from a corpus of 19th-century British and Irish novels, we develop a method to operationalize and quantify domesticity in fiction. Our model captures the nuances of domestic space by analyzing contextual and relational cues rather than relying solely on toponymic and other expl…

A Set of Quebec-French Corpus of Regional Expressions and Terms
David Beauchemin, Yan Tremblay, Mohamed Amine Youssef, Richard Khoury
https://arxiv.org/abs/2510.05026 https://…

A Set of Quebec-French Corpus of Regional Expressions and Terms
The tasks of idiom understanding and dialect understanding are both well-established benchmarks in natural language processing. In this paper, we propose combining them, and using regional idioms as a test of dialect understanding. Towards this end, we propose two new benchmark datasets for the Quebec dialect of French: QFrCoRE, which contains 4,633 instances of idiomatic phrases, and QFrCoRT, which comprises 171 regional instances of idiomatic words. We explain how to construct these corpora, …

Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples
Alexandra Souly, Javier Rando, Ed Chapman, Xander Davies, Burak Hasircioglu, Ezzeldin Shereen, Carlos Mougan, Vasilios Mavroudis, Erik Jones, Chris Hicks, Nicholas Carlini, Yarin Gal, Robert Kirk
https://arxiv.org/abs/2510.07192…

Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples
Poisoning attacks can compromise the safety of large language models (LLMs) by injecting malicious documents into their training data. Existing work has studied pretraining poisoning assuming adversaries control a percentage of the training corpus. However, for large models, even small percentages translate to impractically large amounts of data. This work demonstrates for the first time that poisoning attacks instead require a near-constant number of documents regardless of dataset size. We co…

Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities
Maria Levchenko
https://arxiv.org/abs/2510.06743 https://arx…

Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities
Digital humanities scholars increasingly use Large Language Models for historical document digitization, yet lack appropriate evaluation frameworks for LLM-based OCR. Traditional metrics fail to capture temporal biases and period-specific errors crucial for historical corpus creation. We present an evaluation methodology for LLM-based historical OCR, addressing contamination risks and systematic biases in diplomatic transcription. Using 18th-century Russian Civil font texts, we introduce novel …

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…

Hups, ein bekanntermaßen unsicheres Passwort ergibt eine erstaunlich hohe neunstellige Zahl Treffer:
> This password has been seen 179,863,340 times before in data breaches!
https://haveibeenpwned.com/Passwords

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues
Krish Patel, Dingkun Zhou, Ajay Kankipati, Akshaj Gupta, Zeyi Austin Li, Mohul Shukla, Vibhor Narang, Sara Kofman, Zongli Ye, Grace Wang, Xiaoyu Shi, Tingle Li, Guan-Ting Lin, Kan Jen Cheng, Huang-Cheng Chou, Jiachen Lian, Gopala Anumanchipalli
https://

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues
Emotions conveyed through voice and face shape engagement and context in human-AI interaction. Despite rapid progress in omni-modal large language models (LLMs), the holistic evaluation of emotional reasoning with audiovisual cues remains limited. To address this gap, we introduce AV-EMO-Reasoning, a benchmark designed to systematically assess emotional coherence in LLMs. The framework leverages a curated, single- and multi-turn synthetic audiovisual corpus with a real-world set and is assessed…

Enhancing Speech Emotion Recognition via Fine-Tuning Pre-Trained Models and Hyper-Parameter Optimisation
Aryan Golbaghi, Shuo Zhou
https://arxiv.org/abs/2510.07052 https://

Enhancing Speech Emotion Recognition via Fine-Tuning Pre-Trained Models and Hyper-Parameter Optimisation
We propose a workflow for speech emotion recognition (SER) that combines pre-trained representations with automated hyperparameter optimisation (HPO). Using SpeechBrain wav2vec2-base model fine-tuned on IEMOCAP as the encoder, we compare two HPO strategies, Gaussian Process Bayesian Optimisation (GP-BO) and Tree-structured Parzen Estimators (TPE), under an identical four-dimensional search space and 15-trial budget, with balanced class accuracy (BCA) on the German EmoDB corpus as the objective.…

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…

> if you think about it in the context of the training models—it has a rough sense that you’re like a 37 year old guy on Reddit. That’s the kind of person that it’s doing the continuation for, because that’s a big chunk of the training corpus.
> I often tell people whenever they send me a message like, “a large language model said I should do x, y, z.” what you’re really saying is, “a 37 year old guy on Reddit said it,” and you’ve got roughly the same amount of information

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…

Less LLM, More Documents: Searching for Improved RAG
Jingjie Ning, Yibo Kong, Yunfan Long, Jamie Callan
https://arxiv.org/abs/2510.02657 https://arxiv.org/…

Less LLM, More Documents: Searching for Improved RAG
Retrieval-Augmented Generation (RAG) couples document retrieval with large language models (LLMs). While scaling generators improves accuracy, it also raises cost and limits deployability. We explore an orthogonal axis: enlarging the retriever's corpus to reduce reliance on large LLMs. Experimental results show that corpus scaling consistently strengthens RAG and can often serve as a substitute for increasing model size, though with diminishing returns at larger scales. Small- and mid-sized gen…

Exploring Resolution-Wise Shared Attention in Hybrid Mamba-U-Nets for Improved Cross-Corpus Speech Enhancement
Nikolai Lund K\"uhne, Jesper Jensen, Jan {\O}stergaard, Zheng-Hua Tan
https://arxiv.org/abs/2510.01958

Exploring Resolution-Wise Shared Attention in Hybrid Mamba-U-Nets for Improved Cross-Corpus Speech Enhancement
Recent advances in speech enhancement have shown that models combining Mamba and attention mechanisms yield superior cross-corpus generalization performance. At the same time, integrating Mamba in a U-Net structure has yielded state-of-the-art enhancement performance, while reducing both model size and computational complexity. Inspired by these insights, we propose RWSA-MambaUNet, a novel and efficient hybrid model combining Mamba and multi-head attention in a U-Net structure for improved cros…

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…

SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus
Ming Zhao, Wenhui Dong, Yang Zhang, Xiang Zheng, Zhonghao Zhang, Zian Zhou, Yunzhi Guan, Liukun Xu, Wei Peng, Zhaoyang Gong, Zhicheng Zhang, Dachuan Li, Xiaosheng Ma, Yuli Ma, Jianing Ni, Changjiang Jiang, Lixia Tian, Qixin Chen, Kaishun Xia, Pingping Liu, Tongshun Zhang, Zhiqiang Liu, Zhongan Bi, Chenyang Si, Tiansheng Sun, Caifeng Shan

SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus
Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets. Clinical decision-making for spine disorders requires sophisticated reasoning across X-ray, CT, and MRI at specific vertebral levels. However, progress has been constrained by the absence of traceable, clinically-grounded instruction data and standardized, spine-specific benchmarks. To address this, we introduce Spin…

Quantifying Gender Stereotypes in Japan between 1900 and 1999 with Word Embeddings
Shintaro Sakai, Haewoon Kwak, Jisun An, Akira Matsui
https://arxiv.org/abs/2510.03905 https://…

Quantifying Gender Stereotypes in Japan between 1900 and 1999 with Word Embeddings
We quantify the evolution of gender stereotypes in Japan from 1900 to 1999 using a series of 100 word embeddings, each trained on a corpus from a specific year. We define the gender stereotype value to measure the strength of a word's gender association by computing the difference in cosine similarity of the word to female- versus male-related attribute words. We examine trajectories of gender stereotype across three traditionally gendered domains: Home, Work, and Politics, as well as occupatio…

The Grinch did nothing wrong. He wasn't *stealing* #Christmas, he was just gathering a corpus for training his #AI model. Investors are already lining up with their billions to fund the construction of the Whoville Data Center, ignoring concerns from residents.

ArgoLOOM: agentic AI for fundamental physics from quarks to cosmos
S. D. Bakshi, P. Barry, C. Bissolotti, I. Cloet, S. Corrodi, Z. Djurcic, S. Habib, K. Heitmann, T. J. Hobbs, W. Hopkins, S. Joosten, B. Kriesten, N. Ramachandra, A. Wells, M. Zurek
https://arxiv.org/abs/2510.02426

ArgoLOOM: agentic AI for fundamental physics from quarks to cosmos
Progress in modern physics has been supported by a steadily expanding corpus of numerical analyses and computational frameworks, which in turn form the basis for precision calculations and baseline predictions in experimental programs. These tools play a central role in navigating a complex landscape of theoretical models and current and potential observables to identify and understand fundamental interactions in physics. In addition, efforts to search for new fundamental interactions increasin…

How about “algorithmic stolen corpus derivative” instead of “AI generated”

Neon: Negative Extrapolation From Self-Training Improves Image Generation
Sina Alemohammad, Zhangyang Wang, Richard G. Baraniuk
https://arxiv.org/abs/2510.03597 https://

Neon: Negative Extrapolation From Self-Training Improves Image Generation
Scaling generative AI models is bottlenecked by the scarcity of high-quality training data. The ease of synthesizing from a generative model suggests using (unverified) synthetic data to augment a limited corpus of real data for the purpose of fine-tuning in the hope of improving performance. Unfortunately, however, the resulting positive feedback loop leads to model autophagy disorder (MAD, aka model collapse) that results in a rapid degradation in sample quality and/or diversity. In this pape…

Bin sehr traurig, nicht auf der #FORGE25 zu sein, aber froh, das Philipp Tögel unseren @… würdig vertritt. Wer direkt mal in unseren Ansatz für die TEI-Modellierung heterogener multilingualer Textkorpora reinschnuppern will:

Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data
Stephen Meisenbacher, Svetlozar Nestorov, Peter Norlander
https://arxiv.org/abs/2510.01470

Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data
Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demo…

TyDe 2025:
"Generating a corpus of Hazel programs from ill-typed OCaml programs"
presented by Patrick Ferris, joint work with Anil Madhavapeddy.
https://conf.researchr.org/details/icf

I do love hanging around on a social platform where there simply isn’t any value in all this shit
https://www.theverge.com/news/809349/meta-mark-zuckerberg-ai-social-feeds-q3-2025-earnings

Mark Zuckerberg is excited to add more AI content to all your social feeds
Meta CEO Mark Zuckerberg says AI will add a “huge corpus of content” to social media as the technology makes content easier to create and edit.

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…

Habeas Corpus - Darcy James Argue - Secret Society
#infernalmachines
https://www.youtube.com/watch?v=b0Cw_pLDflY

On Listwise Reranking for Corpus Feedback
Soyoung Yoon, Jongho Kim, Daeyong Kwon, Avishek Anand, Seung-won Hwang
https://arxiv.org/abs/2510.00887 https://a…

On Listwise Reranking for Corpus Feedback
Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a pre-computed document similarity graph in reranking. However, as such graphs are often unavailable, or incur quadratic memory costs even when available, graph-free rerankers leverage large language model (LLM) calls to achieve competitive performance. We introduce L2G, a novel framework that implicitly induces documen…

The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling
Sophie Spliethoff, Sanne Hoeken, Silke Schwandt, Sina Zarrie{\ss}, \"Ozge Ala\c{c}am
https://arxiv.org/abs/2509.22345

The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling
In this paper, we aim at the application of Natural Language Processing (NLP) techniques to historical research endeavors, particularly addressing the study of religious invectives in the context of the Protestant Reformation in Tudor England. We outline a workflow spanning from raw data, through pre-processing and data selection, to an iterative annotation process. As a result, we introduce the InviTE corpus -- a corpus of almost 2000 Early Modern English (EModE) sentences, which are enriched …

ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search
Zeyu Shen, Basileal Imana, Tong Wu, Chong Xiang, Prateek Mittal, Aleksandra Korolova
https://arxiv.org/abs/2509.23519

ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search
Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their outputs in external documents. These systems, however, remain vulnerable to attacks on the retrieval corpus, such as prompt injection. RAG-based search systems (e.g., Google's Search AI Overview) present an interesting setting for studying and protecting against such threats, as defense algorithms can benefit from built-in reliability signals -- like document ranking -- and represent a non-LLM challenge for t…

Rethinking Cross-Corpus Speech Emotion Recognition Benchmarking: Are Paralinguistic Pre-Trained Representations Sufficient?
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish, Swarup Ranjan Behera, Parabattina Bhagath, Pailla Balakrishna Reddy, Arun Balaji Buduru
https://arxiv.org/abs/2509.16182

Rethinking Cross-Corpus Speech Emotion Recognition Benchmarking: Are Paralinguistic Pre-Trained Representations Sufficient?
Recent benchmarks evaluating pre-trained models (PTMs) for cross-corpus speech emotion recognition (SER) have overlooked PTM pre-trained for paralinguistic speech processing (PSP), raising concerns about their reliability, since SER is inherently a paralinguistic task. We hypothesize that PSP-focused PTM will perform better in cross-corpus SER settings. To test this, we analyze state-of-the-art PTMs representations including paralinguistic, monolingual, multilingual, and speaker recognition. Ou…

The Insurrection Act is not a declaration of martial law.
It doesn't shut down the courts.
It doesn't suspend habeas corpus.
It means you can use the military to enforce federal laws,
but the laws themselves remain the same.
https://f…

Trump mulls invoking the Insurrection Act and signals he’ll send troops to San Francisco
President Donald Trump strongly suggested that he was preparing to use the Insurrection Act to crack down on dissent nationwide in an interview …

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

ArabJobs: A Multinational Corpus of Arabic Job Ads
Mo El-Haj
https://arxiv.org/abs/2509.22589 https://arxiv.org/pdf/2509.22589

ArabJobs: A Multinational Corpus of Arabic Job Ads
ArabJobs is a publicly available corpus of Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the United Arab Emirates. Comprising over 8,500 postings and more than 550,000 words, the dataset captures linguistic, regional, and socio-economic variation in the Arab labour market. We present analyses of gender representation and occupational structure, and highlight dialectal variation across ads, which offers opportunities for future research. We also demonstrate applicatio…

Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
Weimin Wu, Alexander C. Furnas, Eddie Yang, Gefei Liu, Akhil Pandey Akella, Xuefeng Song, Dashun Wang, Han Liu
https://arxiv.org/abs/2509.21493

Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
We propose Sci2Pol-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from a scientific paper. We build Sci2Pol-Bench on a five-stage taxonomy to mirror the human writing process: (i) Autocompletion, (ii) Understanding, (iii) Summarization, (iv) Generation, and (v) Verification. It features 18 tasks in multiple-choice and open-ended formats. Specifically, for the Generation stage, we show that…

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…

LibriTTS-VI: A Public Corpus and Novel Methods for Efficient Voice Impression Control
Junki Ohmura, Yuki Ito, Emiru Tsunoo, Toshiyuki Sekiya, Toshiyuki Kumakura
https://arxiv.org/abs/2509.15626

LibriTTS-VI: A Public Corpus and Novel Methods for Efficient Voice Impression Control
Fine-grained control over voice impressions (e.g., making a voice brighter or calmer) is a key frontier for creating more controllable text-to-speech. However, this nascent field faces two key challenges. The first is the problem of impression leakage, where the synthesized voice is undesirably influenced by the speaker's reference audio, rather than the separately specified target impression, and the second is the lack of a public, annotated corpus. To mitigate impression leakage, we propose t…

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…

I have yet to hear any, much less a solid, argument against my suggestion that Presidential pardons and commutations are revocable by a subsequent president.
Were I elected president I would revoke all of El Cheato's pardons and commutations and let the people involved make arguments (probably in the context of Habeas Corpus proceedings) why those actions are not Constitutional.
My own sense is that the question tends more towards the "not revocable" with regard to …

Trump pardons Jan. 6 defendant for a second time over gun conviction
President Donald Trump on Friday pardoned Dan Wilson, a former Jan. 6 defendant, for a second time on gun charges not related to his conduct during the riot at the Capitol on Jan.

Tenyidie Syllabification corpus creation and deep learning applications
Teisovi Angami, Kevisino Khate
https://arxiv.org/abs/2510.00629 https://arxiv.org/p…

Tenyidie Syllabification corpus creation and deep learning applications
The Tenyidie language is a low-resource language of the Tibeto-Burman family spoken by the Tenyimia Community of Nagaland in the north-eastern part of India and is considered a major language in Nagaland. It is tonal, Subject-Object-Verb, and highly agglutinative in nature. Being a low-resource language, very limited research on Natural Language Processing (NLP) has been conducted. To the best of our knowledge, no work on syllabification has been reported for this language. Among the many NLP t…

I hadn't realised that I had any Assembly of Dust but I have Corpus Christi on an old EMusic compilation and very Steely Dan it is. In this case that's a good thing.
#Music #Serendiipity

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…

EuroSpeech: A Multilingual Speech Corpus
Samuel Pfisterer, Florian Gr\"otschla, Luca A. Lanzend\"orfer, Florian Yan, Roger Wattenhofer
https://arxiv.org/abs/2510.00514

EuroSpeech: A Multilingual Speech Corpus
Recent progress in speech processing has highlighted that high-quality performance across languages requires substantial training data for each individual language. While existing multilingual datasets cover many languages, they often contain insufficient data for most languages. Thus, trained models perform poorly on the majority of the supported languages. Our work addresses this challenge by introducing a scalable pipeline for constructing speech datasets from parliamentary recordings. The p…

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…

🇺🇦 #NowPlaying on KEXP's #SeekAndDestroy
Corpus Offal:
🎵 Gorging Gastric Decedent
#CorpusOffal
https://listen.20buckspin.com/track/gorging-gastric-decedent-2
https://open.spotify.com/track/2UmFZG5MaApHRI4zF3uYPm

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…

StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering
Tengjun Ni, Xin Yuan, Shenghong Li, Kai Wu, Ren Ping Liu, Wei Ni, Wenjie Zhang
https://arxiv.org/abs/2510.02827

StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering
Recent progress in retrieval-augmented generation (RAG) has led to more accurate and interpretable multi-hop question answering (QA). Yet, challenges persist in integrating iterative reasoning steps with external knowledge retrieval. To address this, we introduce StepChain GraphRAG, a framework that unites question decomposition with a Breadth-First Search (BFS) Reasoning Flow for enhanced multi-hop QA. Our approach first builds a global index over the corpus; at inference time, only retrieved …

DNABERT-2: Fine-Tuning a Genomic Language Model for Colorectal Gene Enhancer Classification
Darren King, Yaser Atlasi, Gholamreza Rafiee
https://arxiv.org/abs/2509.25274 https:/…

DNABERT-2: Fine-Tuning a Genomic Language Model for Colorectal Gene Enhancer Classification
Gene enhancers control when and where genes switch on, yet their sequence diversity and tissue specificity make them hard to pinpoint in colorectal cancer. We take a sequence-only route and fine-tune DNABERT-2, a transformer genomic language model that uses byte-pair encoding to learn variable-length tokens from DNA. Using assays curated via the Johnston Cancer Research Centre at Queen's University Belfast, we assembled a balanced corpus of 2.34 million 1 kb enhancer sequences, applied summit-c…

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…

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…

Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang. Shuchang Liu, Wenyu Mao, Jiancan Wu, Xun Yang, Xiang Li, Lantao Hu, Han Li, Kun Gai, Xiang Wang
https://arxiv.org/abs/2509.26063

Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models have inspired diffusion-based recommenders, which alleviate sparsity by injecting noise during a forward process to prevent the collapse of perturbed preference distributions. However, current diffusion-based recommenders predominantly rely on continuous Gaussian noise, which is intrinsically mismatched with the discre…

Unpacking Musical Symbolism in Online Communities: Content-Based and Network-Centric Approaches
Kajwan Ziaoddini
https://arxiv.org/abs/2510.00006 https://a…

Unpacking Musical Symbolism in Online Communities: Content-Based and Network-Centric Approaches
This paper examines how musical symbolism is produced and circulated in online communities by combining content-based music analysis with a lightweight network perspective on lyrics. Using a curated corpus of 275 chart-topping songs enriched with audio descriptors (energy, danceability, loudness, liveness, valence, acousticness, speechiness, popularity) and full lyric transcripts, we build a reproducible pipeline that (i) quantifies temporal trends in sonic attributes, (ii) models lexical salie…

WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
Yuhang Dai, Ziyu Zhang, Shuai Wang, Longhao Li, Zhao Guo, Tianlun Zuo, Shuiyuan Wang, Hongfei Xue, Chengyou Wang, Qing Wang, Xin Xu, Hui Bu, Jie Li, Jian Kang, Binbin Zhang, Lei Xie
https://arxiv.org/abs/2509.18004

WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
The scarcity of large-scale, open-source data for dialects severely hinders progress in speech technology, a challenge particularly acute for the widely spoken Sichuanese dialects of Chinese. To address this critical gap, we introduce WenetSpeech-Chuan, a 10,000-hour, richly annotated corpus constructed using our novel Chuan-Pipeline, a complete data processing framework for dialectal speech. To facilitate rigorous evaluation and demonstrate the corpus's effectiveness, we also release high-qual…

I hadn't realised that I had any Assembly of Dust but I have Corpus Christi on an old EMusic compilation and very Steely Dan it is. In this case that's a good thing.
#Music #Serendiipity

Evaluating LLM Generated Detection Rules in Cybersecurity
Anna Bertiger, Bobby Filar, Aryan Luthra, Stefano Meschiari, Aiden Mitchell, Sam Scholten, Vivek Sharath
https://arxiv.org/abs/2509.16749

Evaluating LLM Generated Detection Rules in Cybersecurity
LLMs are increasingly pervasive in the security environment, with limited measures of their effectiveness, which limits trust and usefulness to security practitioners. Here, we present an open-source evaluation framework and benchmark metrics for evaluating LLM-generated cybersecurity rules. The benchmark employs a holdout set-based methodology to measure the effectiveness of LLM-generated security rules in comparison to a human-generated corpus of rules. It provides three key metrics inspired …

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…

LOTUSDIS: A Thai far-field meeting corpus for robust conversational ASR
Pattara Tipaksorn, Sumonmas Thatphithakkul, Vataya Chunwijitra, Kwanchiva Thangthai
https://arxiv.org/abs/2509.18722

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/10]:
- HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Electronic Health Apps
Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, Praveen Rao

ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis
Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili, Azadeh Shakery
https://arxiv.org/abs/2510.10774

ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis
Persian Language, despite being spoken by over 100 million people worldwide, remains severely underrepresented in high-quality speech corpora, particularly for text-to-speech (TTS) synthesis applications. Existing Persian speech datasets are typically smaller than their English counterparts, which creates a key limitation for developing Persian speech technologies. We address this gap by introducing ParsVoice, the largest Persian speech corpus designed specifically for TTS applications. We crea…

SwissGPC v1.0 -- The Swiss German Podcasts Corpus
Samuel Stucki, Mark Cieliebak, Jan Deriu
https://arxiv.org/abs/2509.19866 https://arxiv.org/pdf/2509.1986…

SwissGPC v1.0 -- The Swiss German Podcasts Corpus
We present SwissGPC v1.0, the first mid-to-large-scale corpus of spontaneous Swiss German speech, developed to support research in ASR, TTS, dialect identification, and related fields. The dataset consists of links to talk shows and podcasts hosted on Schweizer Radio und Fernsehen and YouTube, which contain approximately 5400 hours of raw audio. After segmentation and weak annotation, nearly 5000 hours of speech were retained, covering the seven major Swiss German dialect regions alongside Stan…

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…

Visual Authority and the Rhetoric of Health Misinformation: A Multimodal Analysis of Social Media Videos
Mohammad Reza Zarei, Barbara Stead-Coyle, Michael Christensen, Sarah Everts, Majid Komeili
https://arxiv.org/abs/2509.20724

Visual Authority and the Rhetoric of Health Misinformation: A Multimodal Analysis of Social Media Videos
Short form video platforms are central sites for health advice, where alternative narratives mix useful, misleading, and harmful content. Rather than adjudicating truth, this study examines how credibility is packaged in nutrition and supplement videos by analyzing the intersection of authority signals, narrative techniques, and monetization. We assemble a cross platform corpus of 152 public videos from TikTok, Instagram, and YouTube and annotate each on 26 features spanning visual authority, p…

Using Medical Algorithms for Task-Oriented Dialogue in LLM-Based Medical Interviews
Rui Reis, Pedro Rangel Henriques, Jo\~ao Ferreira-Coimbra, Eva Oliveira, Nuno F. Rodrigues
https://arxiv.org/abs/2510.12490

Using Medical Algorithms for Task-Oriented Dialogue in LLM-Based Medical Interviews
We developed a task-oriented dialogue framework structured as a Directed Acyclic Graph (DAG) of medical questions. The system integrates: (1) a systematic pipeline for transforming medical algorithms and guidelines into a clinical question corpus; (2) a cold-start mechanism based on hierarchical clustering to generate efficient initial questioning without prior patient information; (3) an expand-and-prune mechanism enabling adaptive branching and backtracking based on patient responses; (4) a t…

On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search
Nick Hagar, Nicholas Diakopoulos, Jeremy Gilbert
https://arxiv.org/abs/2509.25494

On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search
Investigative journalists routinely confront large document collections. Large language models (LLMs) with retrieval-augmented generation (RAG) capabilities promise to accelerate the process of document discovery, but newsroom adoption remains limited due to hallucination risks, verification burden, and data privacy concerns. We present a journalist-centered approach to LLM-powered document search that prioritizes transparency and editorial control through a five-stage pipeline -- corpus summar…

BeepBank-500: A Synthetic Earcon Mini-Corpus for UI Sound Research and Psychoacoustics Research
Mandip Goswami
https://arxiv.org/abs/2509.17277 https://arx…

BeepBank-500: A Synthetic Earcon Mini-Corpus for UI Sound Research and Psychoacoustics Research
We introduce BeepBank-500, a compact, fully synthetic earcon/alert dataset (300-500 clips) designed for rapid, rights-clean experimentation in human-computer interaction and audio machine learning. Each clip is generated from a parametric recipe controlling waveform family (sine, square, triangle, FM), fundamental frequency, duration, amplitude envelope, amplitude modulation (AM), and lightweight Schroeder-style reverberation. We use three reverberation settings: dry, and two synthetic rooms de…

SiDiaC: Sinhala Diachronic Corpus
Nevidu Jayatilleke, Nisansa de Silva
https://arxiv.org/abs/2509.17912 https://arxiv.org/pdf/2509.17912

SiDiaC: Sinhala Diachronic Corpus
SiDiaC, the first comprehensive Sinhala Diachronic Corpus, covers a historical span from the 5th to the 20th century CE. SiDiaC comprises 58k words across 46 literary works, annotated carefully based on the written date, after filtering based on availability, authorship, copyright compliance, and data attribution. Texts from the National Library of Sri Lanka were digitised using Google Document AI OCR, followed by post-processing to correct formatting and modernise the orthography. The construc…

Learning Relationships Between Separate Audio Tracks for Creative Applications
Balthazar Bujard (IRCAM, SU), J\'er\^ome Nika (IRCAM), F\'ed\'eric Bevilacqua (IRCAM), Nicolas Obin
https://arxiv.org/abs/2509.25296

Learning Relationships Between Separate Audio Tracks for Creative Applications
This paper presents the first step in a research project situated within the field of musical agents. The objective is to achieve, through training, the tuning of the desired musical relationship between a live musical input and a real-time generated musical output, through the curation of a database of separated tracks. We propose an architecture integrating a symbolic decision module capable of learning and exploiting musical relationships from such musical corpus. We detail an offline implem…

LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text
Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Maksim Kuprashevich
https://arxiv.org/abs/2509.21269

LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text
The widespread use of human-like text from Large Language Models (LLMs) necessitates the development of robust detection systems. However, progress is limited by a critical lack of suitable training data; existing datasets are often generated with outdated models, are predominantly in English, and fail to address the increasingly common scenario of mixed human-AI authorship. Crucially, while some datasets address mixed authorship, none provide the character-level annotations required for the pr…

Does Generative Retrieval Overcome the Limitations of Dense Retrieval?
Yingchen Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng
https://arxiv.org/abs/2509.22116

Does Generative Retrieval Overcome the Limitations of Dense Retrieval?
Generative retrieval (GR) has emerged as a new paradigm in neural information retrieval, offering an alternative to dense retrieval (DR) by directly generating identifiers of relevant documents. In this paper, we theoretically and empirically investigate how GR fundamentally diverges from DR in both learning objectives and representational capacity. GR performs globally normalized maximum-likelihood optimization and encodes corpus and relevance information directly in the model parameters, wher…

SloPalSpeech: A 2,8000-Hour Slovak Speech Corpus from Parliamentary Data
Erik Bo\v{z}\'ik, Marek \v{S}uppa
https://arxiv.org/abs/2509.19270 https://arx…

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…

CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis
Xinzhe Xu, Liang Zhao, Hongshen Xu, Chen Chen
https://arxiv.org/abs/2509.21208

CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis
Large Language Models (LLMs) are increasingly tasked with analyzing legal texts and citing relevant statutes, yet their reliability is often compromised by general pre-training that ingests legal texts without specialized focus, obscuring the true depth of their legal knowledge. This paper introduces CLaw, a novel benchmark specifically engineered to meticulously evaluate LLMs on Chinese legal knowledge and its application in reasoning. CLaw comprises two key components: (1) a comprehensive, fi…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/7]:
- ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis
Mohammad Javad Ranjbar Kalahroodi, Heshaam Faili, Azadeh Shakery

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 …

Cross-Dialect Bird Species Recognition with Dialect-Calibrated Augmentation
Jiani Ding, Qiyang Sun, Alican Akman, Bj\"orn W. Schuller
https://arxiv.org/abs/2509.22317 https…

Cross-Dialect Bird Species Recognition with Dialect-Calibrated Augmentation
Dialect variation hampers automatic recognition of bird calls collected by passive acoustic monitoring. We address the problem on DB3V, a three-region, ten-species corpus of 8-s clips, and propose a deployable framework built on Time-Delay Neural Networks (TDNNs). Frequency-sensitive normalisation (Instance Frequency Normalisation and a gated Relaxed-IFN) is paired with gradient-reversal adversarial training to learn region-invariant embeddings. A multi-level augmentation scheme combines wavefo…

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
Cameron Morin, Matti Marttinen Larsson
https://arxiv.org/abs/2510.12306

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable, unsupervised pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc…

UPRPRC: Unified Pipeline for Reproducing Parallel Resources -- Corpus from the United Nations
Qiuyang Lu, Fangjian Shen, Zhengkai Tang, Qiang Liu, Hexuan Cheng, Hui Liu, Wushao Wen
https://arxiv.org/abs/2509.15789

UPRPRC: Unified Pipeline for Reproducing Parallel Resources -- Corpus from the United Nations
The quality and accessibility of multilingual datasets are crucial for advancing machine translation. However, previous corpora built from United Nations documents have suffered from issues such as opaque process, difficulty of reproduction, and limited scale. To address these challenges, we introduce a complete end-to-end solution, from data acquisition via web scraping to text alignment. The entire process is fully reproducible, with a minimalist single-machine example and optional distribute…

Emotion-Disentangled Embedding Alignment for Noise-Robust and Cross-Corpus Speech Emotion Recognition
Upasana Tiwari, Rupayan Chakraborty, Sunil Kumar Kopparapu
https://arxiv.org/abs/2510.09072

Emotion-Disentangled Embedding Alignment for Noise-Robust and Cross-Corpus Speech Emotion Recognition
Effectiveness of speech emotion recognition in real-world scenarios is often hindered by noisy environments and variability across datasets. This paper introduces a two-step approach to enhance the robustness and generalization of speech emotion recognition models through improved representation learning. First, our model employs EDRL (Emotion-Disentangled Representation Learning) to extract class-specific discriminative features while preserving shared similarities across emotion categories. N…

Efficient and Versatile Model for Multilingual Information Retrieval of Islamic Text: Development and Deployment in Real-World Scenarios
Vera Pavlova, Mohammed Makhlouf
https://arxiv.org/abs/2509.15380

Efficient and Versatile Model for Multilingual Information Retrieval of Islamic Text: Development and Deployment in Real-World Scenarios
Despite recent advancements in Multilingual Information Retrieval (MLIR), a significant gap remains between research and practical deployment. Many studies assess MLIR performance in isolated settings, limiting their applicability to real-world scenarios. In this work, we leverage the unique characteristics of the Quranic multilingual corpus to examine the optimal strategies to develop an ad-hoc IR system for the Islamic domain that is designed to satisfy users' information needs in multiple la…

Hierarchical Indexing with Knowledge Enrichment for Multilingual Video Corpus Retrieval
Yu Wang, Tianhao Tan, Yifei Wang
https://arxiv.org/abs/2510.09553 https://

Hierarchical Indexing with Knowledge Enrichment for Multilingual Video Corpus Retrieval
Retrieving relevant instructional videos from multilingual medical archives is crucial for answering complex, multi-hop questions across language boundaries. However, existing systems either compress hour-long videos into coarse embeddings or incur prohibitive costs for fine-grained matching. We tackle the Multilingual Video Corpus Retrieval (mVCR) task in the NLPCC-2025 M4IVQA challenge with a multi-stage framework that integrates multilingual semantics, domain terminology, and efficient long-…

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…

NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Yuqing Zhang, Ecesu \"Urker, Tessa Verhoef, Gemma Boleda, Arianna Bisazza
https://arxiv.org/abs/2509.22479

NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeling) cannot get at causal mechanisms, and experimental paradigms with humans are hard to apply to semantic change due to the extended diachronic processes involved. This work introduces NeLLCom-Lex, a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system (e.g. En…

CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models
Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski
https://arxiv.org/abs/2509.22360

CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models
Large language models (LLMs) excel at operating at scale by leveraging social media and various data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM's ability to contextualize semantic and normative evolution of language and to capture diachronic variation. To support analysis and training for the latter, we introduce CHRONOBERG, a temporally structured corpus of English book texts spanning 250 years, curat…

SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Yizhou Wang, Chen Tang, Han Deng, Jiabei Xiao, Jiaqi Liu, Jianyu Wu, Jun Yao, Pengze Li, Encheng Su, Lintao Wang, Guohang Zhuang, Yuchen Ren, Ben Fei, Ming Hu, Xin Chen, Dongzhan Zhou, Junjun He, Xiangyu Yue, Zhenfei Yin, Jiamin Wu, Qihao Zheng, Yuhao Zhou, Huihui Xu, Chenglong Ma, Yan Lu, Wenlong Zhang, Chunfeng Song, Philip Torr, Shixiang Tang, Xinzhu Ma, Wanli Ouyang, Lei Bai

SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanning scientific text, pure sequences, and sequence-text pairs, then aligned via SFT on 40M instructions, annealed cold-start bootstrapping to elicit long-form chain-of-thought, and reinforcement learning with task-specific reward shaping, which instills deliberate scientific reasoning. It supports four capability famili…

Charting a Decade of Computational Linguistics in Italy: The CLiC-it Corpus
Chiara Alzetta, Serena Auriemma, Alessandro Bondielli, Luca Dini, Chiara Fazzone, Alessio Miaschi, Martina Miliani, Marta Sartor
https://arxiv.org/abs/2509.19033

TianHui: A Domain-Specific Large Language Model for Diverse Traditional Chinese Medicine Scenarios
Ji Yin, Menglan He, Yujie Zhang, Linshuai Zhang, Tingting Ma, Ce Tian, Jie Wu, Lin Xu, Tao Jiang
https://arxiv.org/abs/2509.19834

TianHui: A Domain-Specific Large Language Model for Diverse Traditional Chinese Medicine Scenarios
Domain-specific LLMs in TCM face limitations in research settings due to constrained adaptability, insufficient evaluation datasets, and limited computational resources. This study presents TianHui, a specialized TCM LLM built through contextual data integration and domain knowledge fusion. We constructed a large-scale TCM corpus (0.97GB unsupervised data + 611,312 QA pairs) and employed a two-stage training strategy with QLoRA, DeepSpeed Stage 2, and Flash Attention 2. Evaluation on 12 benchma…

HICode: Hierarchical Inductive Coding with LLMs
Mian Zhong, Pristina Wang, Anjalie Field
https://arxiv.org/abs/2509.17946 https://arxiv.org/pdf/2509.17946

HICode: Hierarchical Inductive Coding with LLMs
Despite numerous applications for fine-grained corpus analysis, researchers continue to rely on manual labeling, which does not scale, or statistical tools like topic modeling, which are difficult to control. We propose that LLMs have the potential to scale the nuanced analyses that researchers typically conduct manually to large text corpora. To this effect, inspired by qualitative research methods, we develop HICode, a two-part pipeline that first inductively generates labels directly from an…

Causal Understanding by LLMs: The Role of Uncertainty
Oscar Lithgow-Serrano, Vani Kanjirangat, Alessandro Antonucci
https://arxiv.org/abs/2509.20088 https://

Causal Understanding by LLMs: The Role of Uncertainty
Recent papers show LLMs achieve near-random accuracy in causal relation classification, raising questions about whether such failures arise from limited pretraining exposure or deeper representational gaps. We investigate this under uncertainty-based evaluation, testing whether pretraining exposure to causal examples improves causal understanding >18K PubMed sentences -- half from The Pile corpus, half post-2024 -- across seven models (Pythia-1.4B/7B/12B, GPT-J-6B, Dolly-7B/12B, Qwen-7B). We an…

CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems
Soham Bhattacharjee, Mukund K Roy, Yathish Poojary, Bhargav Dave, Mihir Raj, Vandan Mujadia, Baban Gain, Pruthwik Mishra, Arafat Ahsan, Parameswari Krishnamurthy, Ashwath Rao, Gurpreet Singh Josan, Preeti Dubey, Aadil Amin Kak, Anna Rao Kulkarni, Narendra VG, Sunita Arora, Rakesh Balbantray, Prasenjit Majumdar, Karunesh K Arora, Asif Ekbal, Dipti Mishra Sharma

CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems
India's linguistic landscape is one of the most diverse in the world, comprising over 120 major languages and approximately 1,600 additional languages, with 22 officially recognized as scheduled languages in the Indian Constitution. Despite recent progress in multilingual neural machine translation (NMT), high-quality parallel corpora for Indian languages remain scarce, especially across varied domains. In this paper, we introduce a large-scale, high-quality annotated parallel corpus covering 1…

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…

Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment
Ke Wang, Wenning Wei, Yan Deng, Lei He, Sheng Zhao
https://arxiv.org/abs/2509.15701 https://

Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment
Automatic Pronunciation Assessment (APA) is critical for Computer-Assisted Language Learning (CALL), requiring evaluation across multiple granularities and aspects. Large Multimodal Models (LMMs) present new opportunities for APA, but their effectiveness in fine-grained assessment remains uncertain. This work investigates fine-tuning LMMs for APA using the Speechocean762 dataset and a private corpus. Fine-tuning significantly outperforms zero-shot settings and achieves competitive results on si…

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 …

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…

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…

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…

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…

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…

KORMo: Korean Open Reasoning Model for Everyone
Minjun Kim, Hyeonseok Lim, Hangyeol Yoo, Inho Won, Seungwoo Song, Minkyung Cho, Junhun Yuk, Changsu Choi, Dongjae Shin, Huige Lee, Hoyun Song, Alice Oh, Kyungtae Lim
https://arxiv.org/abs/2510.09426

KORMo: Korean Open Reasoning Model for Everyone
This work presents the first large-scale investigation into constructing a fully open bilingual large language model (LLM) for a non-English language, specifically Korean, trained predominantly on synthetic data. We introduce KORMo-10B, a 10.8B-parameter model trained from scratch on a Korean-English corpus in which 68.74% of the Korean portion is synthetic. Through systematic experimentation, we demonstrate that synthetic data, when carefully curated with balanced linguistic coverage and diver…