Tootfinder

MELAC: Massive Evaluation of Large Language Models with Alignment of Culture in Persian Language
Farhan Farsi, Farnaz Aghababaloo, Shahriar Shariati Motlagh, Parsa Ghofrani, MohammadAli SadraeiJavaheri, Shayan Bali, Amirhossein Shabani, Farbod Bijary, Ghazal Zamaninejad, AmirMohammad Salehoof, Saeedeh Momtazi
https://arxiv.org/abs/2508.006…

MELAC: Massive Evaluation of Large Language Models with Alignment of Culture in Persian Language
As large language models (LLMs) become increasingly embedded in our daily lives, evaluating their quality and reliability across diverse contexts has become essential. While comprehensive benchmarks exist for assessing LLM performance in English, there remains a significant gap in evaluation resources for other languages. Moreover, because most LLMs are trained primarily on data rooted in European and American cultures, they often lack familiarity with non-Western cultural contexts. To address …

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining
Zhixun Chen, Ping Guo, Wenhan Han, Yifan Zhang, Binbin Liu, Haobin Lin, Fengze Liu, Yan Zhao, Bingni Zhang, Taifeng Wang, Yin Zheng, Meng Fang
https://arxiv.org/abs/2507.01785

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining
Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English. We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a single rater for 17 target languages. MuRating aggregates multiple English "raters" via pairwise comparisons to learn unified document-quality scores,then projects these judgments through translation to train a multilingual evaluator on mon…

wordnet: WordNet relationships
A network of English words from the WordNet. Node is a word, and edge denotes relationships between words (synonymy, hyperonymy, meronymy, etc.). The date at which this network was extracted from WordNet is not unknown.
This network has 146005 nodes and 656999 edges.
Tags: Informational, Language, Unweighted
htt…

SESGO: Spanish Evaluation of Stereotypical Generative Outputs
Melissa Robles, Catalina Bernal, Denniss Raigoso, Mateo Dulce Rubio
https://arxiv.org/abs/2509.03329 https://

SESGO: Spanish Evaluation of Stereotypical Generative Outputs
This paper addresses the critical gap in evaluating bias in multilingual Large Language Models (LLMs), with a specific focus on Spanish language within culturally-aware Latin American contexts. Despite widespread global deployment, current evaluations remain predominantly US-English-centric, leaving potential harms in other linguistic and cultural contexts largely underexamined. We introduce a novel, culturally-grounded framework for detecting social biases in instruction-tuned LLMs. Our approa…

Teaching a Language Model to Speak the Language of Tools
Simeon Emanuilov
https://arxiv.org/abs/2506.23394 https://arxiv.org/pdf/2506…

Teaching a Language Model to Speak the Language of Tools
External tool integration through function-calling is essential for practical language model applications, yet most multilingual models lack reliable tool-use capabilities in non-English languages. Even state-of-the-art multilingual models struggle with determining when to use tools and generating the structured outputs required for function calls, often exhibiting language confusion when prompted in lower-resource languages. This work presents a methodology for adapting existing language model…

Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition
Mayur Shirke, Amey Shembade, Pavan Thorat, Madhushri Wagh, Raviraj Joshi
https://arxiv.org/abs/2509.02514

Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition
Named Entity Recognition (NER) in code-mixed text, particularly Hindi-English (Hinglish), presents unique challenges due to informal structure, transliteration, and frequent language switching. This study conducts a comparative evaluation of code-mixed fine-tuned models and non-code-mixed multilingual models, along with zero-shot generative large language models (LLMs). Specifically, we evaluate HingBERT, HingMBERT, and HingRoBERTa (trained on code-mixed data), and BERT Base Cased, IndicBERT, R…

The Manipulative Power of Voice Characteristics: Investigating Deceptive Patterns in Mandarin Chinese Female Synthetic Speech
Shuning Zhang (Tsinghua University, Beijing, China), Han Chen (Wuhan Institute of Technology, Wuhan, China), Yabo Wang (Tsinghua University, Beijing, China), Yiqun Xu (Tsinghua University, Beijing, China), Jiaqi Bai (Tsinghua University, Beijing, China), Yuanyuan Wu (Shanghai Jiaotong University, Shanghai, China), Shixuan Li (Tsinghua University, Beijing, China)…

The Manipulative Power of Voice Characteristics: Investigating Deceptive Patterns in Mandarin Chinese Female Synthetic Speech
Pervasive voice interaction enables deceptive patterns through subtle voice characteristics, yet empirical investigation into this manipulation lags behind, especially within major non-English language contexts. Addressing this gap, our study presents the first systematic investigation into voice characteristic-based dark patterns employing female synthetic voices in Mandarin Chinese. This focus is crucial given the prevalence of female personas in commercial assistants and the prosodic signifi…

"The Most Important Word in the English Language" #connect

Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suite
William H English, Chase Walker, Dominic Simon, Sumit Kumar Jha, Rickard Ewetz
https://arxiv.org/abs/2507.00877

Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suite
Empirical evaluation of state-of-the-art natural-language (NL) to temporal-logic (TL) translation systems reveals near-perfect performance on existing benchmarks. However, current studies measure only the accuracy of the translation of NL logic into formal TL, ignoring a system's capacity to ground atomic propositions into new scenarios or environments. This is a critical feature, necessary for the verification of resulting formulas in a concrete state space. Consequently, most NL-to-TL transla…

wiki_article_words: Wikipedia article-words (en) (2010)
A bipartite network of English Wikipedia articles and the words they contain. The edge weight gives the number of times a word appeared in the connected article.
This network has 276739 nodes and 2941902 edges.
Tags: Informational, Language, Weighted
https://

German-English connections: "anecken" and "to egg on." These words don't mean exactly the same thing, but they are related. Etymologically, they seem closely related. English is a Germanic language. And phrasal verbs offer up interesting connections.
#language #linguistics

5th Hawaii International Conference on English Language and Literature Studies (HICELLS 2026)
https://ift.tt/KS5dAaj
updated: Monday, August 4, 2025 - 11:40amfull name / name of organization: Francisco P.…
via Input 4 RELCFP

As I'm learning Dutch, I'm reminded that the idea that there are people who believe that the bible is to be taken literally. The idea that a several hundred year old translation of a collection of texts in multiple languages, that were themselves translated multiple times between languages, before the whole thing was translated to Latin, then being translated to English, could somehow perfectly reflect the original text... Yeah, it's only possible to believe that if you have no idea how languages work and have never learned another language.
Like, just from linguistic drift alone if the bible were written in King James English you're losing *so* much context. But Hebrew, Aramaic, and Greek translated to Latin, then to English, then to English again?
There are so many things that erg can't be translated, even as a beginner. Dutch and English are two of the closest languages that exist, they're both Germanic languages and they're the closest to each other (other than Friesian). You can't really be much closer, and yet, there are so many things you can't mutually represent. Hebrew and Latin, Aramaic and Latin, Latin and English, Greek and English, these aren't even the same families at all... They're extremely distant. There's absolutely no way to represent concepts from one to another without another book's worth of explanation.
And that ignores all the cultural context, which is mostly lost and a library and decade of education to get the stuff that we *do* know.
Only monolingual Americans could come up with an idea so incredibly asinine.

Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages
Samridhi Raj Sinha, Rajvee Sheth, Abhishek Upperwal, Mayank Singh
https://arxiv.org/abs/2507.01853

Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages
The rapid advancement of Large Language Models (LLMs) has intensified the need for evaluation frameworks that go beyond English centric benchmarks and address the requirements of linguistically diverse regions such as India. We present EKA-EVAL, a unified and production-ready evaluation framework that integrates over 35 benchmarks, including 10 Indic-specific datasets, spanning categories like reasoning, mathematics, tool use, long-context understanding, and reading comprehension. Compared to e…

MetaCLIP 2: A Worldwide Scaling Recipe
Yung-Sung Chuang, Yang Li, Dong Wang, Ching-Feng Yeh, Kehan Lyu, Ramya Raghavendra, James Glass, Lifei Huang, Jason Weston, Luke Zettlemoyer, Xinlei Chen, Zhuang Liu, Saining Xie, Wen-tau Yih, Shang-Wen Li, Hu Xu
https://arxiv.org/abs/2507.22062

MetaCLIP 2: A Worldwide Scaling Recipe
Contrastive Language-Image Pretraining (CLIP) is a popular foundation model, supporting from zero-shot classification, retrieval to encoders for multimodal large language models (MLLMs). Although CLIP is successfully trained on billion-scale image-text pairs from the English world, scaling CLIP's training further to learning from the worldwide web data is still challenging: (1) no curation method is available to handle data points from non-English world; (2) the English performance from existin…

word_adjacency: Word Adjacency Networks
Directed Networks of word adjacency in texts of several languages including English, French, Spanish and Japanese.
This network has 7381 nodes and 46281 edges.
Tags: Informational, Language, Unweighted
https://networks.skewed.de/net/word_ad

GHTM: A Graph based Hybrid Topic Modeling Approach in Low-Resource Bengali Language
Farhana Haque, Md. Abdur Rahman, Sumon Ahmed
https://arxiv.org/abs/2508.00605 https://…

GHTM: A Graph based Hybrid Topic Modeling Approach in Low-Resource Bengali Language
Topic modeling is a Natural Language Processing (NLP) technique that is used to identify latent themes and extract topics from text corpora by grouping similar documents based on their most significant keywords. Although widely researched in English, topic modeling remains understudied in Bengali due to its morphological complexity, lack of adequate resources and initiatives. In this contribution, a novel Graph Convolutional Network (GCN) based model called GHTM (Graph-Based Hybrid Topic Model)…

English is an amazing language. We have two similar if folksy / archaic expressions: "to cotton to" and "to cotton on to". They mean different things. https://www.phrases.org.uk/meanings/cotton-on.html

You Sound a Little Tense: L2 Tailored Clear TTS Using Durational Vowel Properties
Paige Tutt\"os\'i, H. Henny Yeung, Yue Wang, Jean-Julien Aucouturier, Angelica Lim
https://arxiv.org/abs/2506.23367

You Sound a Little Tense: L2 Tailored Clear TTS Using Durational Vowel Properties
We present the first text-to-speech (TTS) system tailored to second language (L2) speakers. We use duration differences between American English tense (longer) and lax (shorter) vowels to create a "clarity mode" for Matcha-TTS. Our perception studies showed that French-L1, English-L2 listeners had fewer (at least 9.15%) transcription errors when using our clarity mode, and found it more encouraging and respectful than overall slowed down speech. Remarkably, listeners were not aware of these eff…

Mitigating Language Mismatch in SSL-Based Speaker Anonymization
Zhe Zhang, Wen-Chin Huang, Xin Wang, Xiaoxiao Miao, Junichi Yamagishi
https://arxiv.org/abs/2507.00458

Mitigating Language Mismatch in SSL-Based Speaker Anonymization
Speaker anonymization aims to protect speaker identity while preserving content information and the intelligibility of speech. However, most speaker anonymization systems (SASs) are developed and evaluated using only English, resulting in degraded utility for other languages. This paper investigates language mismatch in SASs for Japanese and Mandarin speech. First, we fine-tune a self-supervised learning (SSL)-based content encoder with Japanese speech to verify effective language adaptation. T…

IRS considers eliminating ability to file your taxes in language other than English | The Independent
https://www.independent.co.uk/news/world/americas/us-politics/irs-english-tax-filing-trump-b2795612.html

Recommendations to overcome language barriers in the Vera C. Rubin Observatory Research Ecosystem
Jos\'e Antonio Alonso Pav\'on, Andr\'es Alejandro Plazas Malag\'on
https://arxiv.org/abs/2507.18682

Recommendations to overcome language barriers in the Vera C. Rubin Observatory Research Ecosystem
The report presents a comprehensive set of five recommendations to reduce language barriers within the Vera C. Rubin Observatory Research Ecosystem, promoting greater inclusion of researchers who are speakers of English as an additional language. Recognizing that English linguistic hegemony in science limits participation and productivity, the document proposes multilingual presentation formats, academic writing training, a Virtual Writing Center, language support programs, and writing retreats…

The Kyiv Independent crosses 20,000 paying members, up from 17,500 in May, with ~70% of its revenue coming from readers, and has no plans to add a paywall (Sarah Scire/Nieman Lab)
https://www.niemanlab.org/2025/07/how-kyiv-independent-reac…

How the Kyiv Independent reached 20,000 paying members — with no paywall
Horizontal connections and membership community have kept subscriber churn for the English-language publication under 2%.

wiki_talk: Wikipedia talk networks
Interactions among users of 10 language-specific Wikipedias: Arabic, Chinese, Dutch, English, French, German, Italian, Portuguese, Russian, and Spanish. Nodes are registered wiki editors, and an edge represents a user i having written a message on user j's talk page. Edges are timestamped. The precise dates of the snapshots are uncertain.
This network has 8097 nodes and 63809 edges.
Tags: Social, Communication, Unweighted, Multigraph, …

Dear Media,
The story is that he’s a fascist, not that he’s ignorant.
Thank you. https://frontrange.co/@apnewsbot/114828593247109938

Contrasting Cognitive Styles in Vision-Language Models: Holistic Attention in Japanese Versus Analytical Focus in English
Ahmed Sabir, Azinovi\v{c} Gasper, Mengsay Loem, Rajesh Sharma
https://arxiv.org/abs/2507.00700

Contrasting Cognitive Styles in Vision-Language Models: Holistic Attention in Japanese Versus Analytical Focus in English
Cross-cultural research in perception and cognition has shown that individuals from different cultural backgrounds process visual information in distinct ways. East Asians, for example, tend to adopt a holistic perspective, attending to contextual relationships, whereas Westerners often employ an analytical approach, focusing on individual objects and their attributes. In this study, we investigate whether Vision-Language Models (VLMs) trained predominantly on different languages, specifically …

I wonder if one day someone can explain to me why Google thinks the fact that I am in a foreign country means I immediately am fluent in that country's language, and prefer it over English or my native language. I mean, there's an Accept-Language header, I think it's older than Google.

You'll never become a NATIVE English speaker
No matter how hard you try, the years in the UK or Ireland, the effort in your accent, or the AI applications you might use to fake it
There is a language wall, made of accents, cultural references and seemingly illogical phrasal verbs and idioms, that we cannot jump
But IT DOESN'T MATTER.
90% of your interactions are probably with other non-native speakers. As long as you understand each other, you're good.

Neither British nor American: how Europeans speak their own flavour of English - The European Perspective
Learning English with Elton John

Language learning has been part of me since high school. I'm solid in 2 non-English languages, crappy but survivable in 2 others. I've played with & started learning others many times.
I'm real busy rn, but language learning could be a fun thing to do for myself & make me feel like I'm still me.
But I'm stumped about my language picks. I learnt the obvious European languages in school; later tried key Asian languages. What do I want to do now?
African languages? I won't be getting a chance to use them much in Aus, & I'm unlikely to get to a stage where I can read literature.
I tried Slovenian/Slovene on a whim & really love it, but I'll never go there. Is the practical but unfun answer grind out more kanji/hanzi? Or is whimsically learning a language spoken by only 2.5 million people reasonable? I will continue struggling through with Ukrainian, 'cause I think it's important.
#LanguageLearning

Trump says the president of Liberia, where English is the official language, speaks 'good English' (NBC News)
https://www.nbcnews.com/politics/donald-trump/trump-says-president-liberia-english-official-language-speaks-good-eng-rcna217827
http://www.memeorandum.com/250709/p106#a250709p106

EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Sanchit Ahuja, Praneetha Vaddamanu, Barun Patra
https://arxiv.org/abs/2507.00246

EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Despite recent advances in Language Reasoning Models (LRMs), most research focuses solely on English, even though many models are pretrained on multilingual data. In this work, we investigate: Is English the most token-efficient language for reasoning? We evaluate three open-source RLMs: DeepSeek R1, Qwen 2.5 and Qwen 3, across four math datasets and seven typologically diverse languages. We find that reasoning in non-English languages not only reduces token usage, but also preserves accuracy. …

Linguine: A Natural-Language Programming Language with Formal Semantics and a Clean Compiler Pipeline
Lifan Hu
https://arxiv.org/abs/2506.08396 https://

Linguine: A Natural-Language Programming Language with Formal Semantics and a Clean Compiler Pipeline
Linguine is a natural-language-inspired programming language that enables users to write programs in a fluent, controlled subset of English while preserving formal semantics. The language introduces anaphoric constructs, such as pronoun variables (e.g., "it", "them"), that are statically resolved through referent-tracking analysis combined with a Hindley-Milner-style type system. Each pronoun is guaranteed to be unambiguous and well-typed at compile time. The Linguine compiler pipeline includ…

Liberians angered by Trump’s praise for leader
There was confusion and anger in Liberia on Thursday after Donald Trump praised the English skills of President Joseph Boakai.
“Such good English,” Trump said to Boakai, with visible surprise. “Such beautiful English.”
👉 English has been the west African nation’s official language since the 1800s.
But Trump did not stop there.
“Where did you learn to speak so beautifully?” he continued, as Boakai murmured a respo…

Metadata Enrichment of Long Text Documents using Large Language Models
Manika Lamba, You Peng, Sophie Nikolov, Glen Layne-Worthey, J. Stephen Downie
https://arxiv.org/abs/2506.20918

Metadata Enrichment of Long Text Documents using Large Language Models
In this project, we semantically enriched and enhanced the metadata of long text documents, theses and dissertations, retrieved from the HathiTrust Digital Library in English published from 1920 to 2020 through a combination of manual efforts and large language models. This dataset provides a valuable resource for advancing research in areas such as computational social science, digital humanities, and information science. Our paper shows that enriching metadata using LLMs is particularly benef…

I'm not opposed to neologisms. To the contrary, I do love them, sometimes coining my own or adapting happily. That is, as long as they make the language richer, or perhaps more precise.
What I truly hate is the modern goo that people are speaking, because they don't know their own language well. The business newspeak, so to say.
This is especially bad in Polish where people are randomly polonizing English words for no reason at all.

UK State, dancing to Garage's agenda, announces yet more cruelty to people fleeing persecution.
Families of those granted asylum will face
tougher English language standards and a test of financial resources.
Imagine doing that during the Uganda or Kosovo emergencies.
https://www.bbc.co.uk/news/article…

UK to tighten family member rules for asylum cases
Yvette Cooper will outline her plans when MPs return to Parliament after the summer break.

Brains and language models converge on a shared conceptual space across different languages
Zaid Zada, Samuel A Nastase, Jixing Li, Uri Hasson
https://arxiv.org/abs/2506.20489

Brains and language models converge on a shared conceptual space across different languages
Human languages differ widely in their forms, each having distinct sounds, scripts, and syntax. Yet, they can all convey similar meaning. Do different languages converge on a shared neural substrate for conceptual meaning? We used language models (LMs) and naturalistic fMRI to identify neural representations of the shared conceptual meaning of the same story as heard by native speakers of three languages: English, Chinese, and French. We found that LMs trained on entirely different languages co…

Woah, amazing! Valve is changing reviews so bombs in one region don't tank it everywhere.
> When there are enough reviews written in a particular language, Steam will calculate a review score for that language. The Review Score displayed to users will be based on their primary language. What this means is that some languages may show more positive review scores, while others may show more negative ones, for the same game.

Steam News - Updates to User Review Scores Based on Language - Steam News
The review score for some games is now language-specific

Junior Communications Manager: https://benborges.xyz/2025/08/22/junior-communications-manager.html

English-language trailer for "Quartet" just dropped!
https://chaos.social/@theateru34/114742812225916947

Natural language processing for African languages
David Ifeoluwa Adelani
https://arxiv.org/abs/2507.00297 https://arxiv.org/pdf/2507.…

Natural language processing for African languages
Recent advances in word embeddings and language models use large-scale, unlabelled data and self-supervised learning to boost NLP performance. Multilingual models, often trained on web-sourced data like Wikipedia, face challenges: few low-resource languages are included, their data is often noisy, and lack of labeled datasets makes it hard to evaluate performance outside high-resource languages like English. In this dissertation, we focus on languages spoken in Sub-Saharan Africa where all the …

Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens
Fred Mutisya (Qhala, Kenya Medical Association), Shikoh Gitau (Qhala), Christine Syovata (Kenya Medical Association), Diana Oigara (Kenya Medical Association), Ibrahim Matende (Kenya Medical Association), Muna Aden (Kenya Medical Association), Munira Ali (Kenya Medical Association), Ryan Nyotu (Kenya Medical Association), Diana Marion (Kenya Medical Asso…

Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens
Introduction: Existing medical LLM benchmarks largely reflect examination syllabi and disease profiles from high income settings, raising questions about their validity for African deployment where malaria, HIV, TB, sickle cell disease and other neglected tropical diseases (NTDs) dominate burden and national guidelines drive care. Methodology: We systematically reviewed 31 quantitative LLM evaluation papers (Jan 2019 May 2025) identifying 19 English medical QA benchmarks. Alama Health QA was de…

This just occured to me (too much sun and gin lemonade could be a factor): English is a funny language and when they say Artificial they mean Automated, and when they say Intelligence they don't mean smarts, they mean covertly gathering intel from prospective enemies!
Hence #ArtificialIntelligence, often promoted to General.
The purpose of any system is what it does, not what it consistently fails to do.

“Cellar door” is—according to Tolkien—the most beautiful combination of words in the English language.
But what’s the worst?

I made a new friend today in Albion Europe.
ZairGT...I think native language is Spanish but also speaks English.
I found this Tree T4.3 in a blue zone and I went to it back and forth. At one point I find ZairGT(both him and me unflagged, no FW) there...and the gathering duel was "short and bloody" and I realized I have no chance to take that.
I admitted defeat and often went there and did the 1 sign and WP...
part 2 soon...

A Financial Brain Scan of the LLM
Hui Chen, Antoine Didisheim, Luciano Somoza, Hanqing Tian
https://arxiv.org/abs/2508.21285 https://arxiv.org/pdf/2508.212…

A Financial Brain Scan of the LLM
Emerging techniques in computer science make it possible to "brain scan" large language models (LLMs), identify the plain-English concepts that guide their reasoning, and steer them while holding other factors constant. We show that this approach can map LLM-generated economic forecasts to concepts such as sentiment, technical analysis, and timing, and compute their relative importance without reducing performance. We also show that models can be steered to be more or less risk-averse, optimist…

Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation
Elijah Kayode Adejumo, Brittany Johnson, Mariam Guizani
https://arxiv.org/abs/2508.02497

Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation
While open source communities attract diverse contributors globally, few repositories provide essential documentation in languages other than English. Large language models (LLMs) have demonstrated remarkable capabilities in software engineering tasks and translations across domains. However, little is known about LLM capabilities in translating open-source technical documentation, which mixes natural language, code, URLs, and markdown formatting. To understand the need and potential for LLMs i…

How Instruction-Tuning Imparts Length Control: A Cross-Lingual Mechanistic Analysis
Elisabetta Rocchetti, Alfio Ferrara
https://arxiv.org/abs/2509.02075 https://

How Instruction-Tuning Imparts Length Control: A Cross-Lingual Mechanistic Analysis
Adhering to explicit length constraints, such as generating text with a precise word count, remains a significant challenge for Large Language Models (LLMs). This study aims at investigating the differences between foundation models and their instruction-tuned counterparts, on length-controlled text generation in English and Italian. We analyze both performance and internal component contributions using Cumulative Weighted Attribution, a metric derived from Direct Logit Attribution. Our finding…

wiki_talk: Wikipedia talk networks
Interactions among users of 10 language-specific Wikipedias: Arabic, Chinese, Dutch, English, French, German, Italian, Portuguese, Russian, and Spanish. Nodes are registered wiki editors, and an edge represents a user i having written a message on user j's talk page. Edges are timestamped. The precise dates of the snapshots are uncertain.
This network has 41452 nodes and 131884 edges.
Tags: Social, Communication, Unweighted, Multigraph…

RhythmTA: A Visual-Aided Interactive System for ESL Rhythm Training via Dubbing Practice
Chang Chen, Sicheng Song, Shuchang Xu, Zhicheng Li, Huamin Qu, Yanna Lin
https://arxiv.org/abs/2507.19026

RhythmTA: A Visual-Aided Interactive System for ESL Rhythm Training via Dubbing Practice
English speech rhythm, the temporal patterns of stressed syllables, is essential for English as a second language (ESL) learners to produce natural-sounding and comprehensible speech. Rhythm training is generally based on imitation of native speech. However, it relies heavily on external instructor feedback, preventing ESL learners from independent practice. To address this gap, we present RhythmTA, an interactive system for ESL learners to practice speech rhythm independently via dubbing, an i…

MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations
Qiyao Xue, Yuchen Dou, Ryan Shi, Xiang Lorraine Li, Wei Gao
https://arxiv.org/abs/2508.00760

MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations
Hate speech detection on Chinese social networks presents distinct challenges, particularly due to the widespread use of cloaking techniques designed to evade conventional text-based detection systems. Although large language models (LLMs) have recently improved hate speech detection capabilities, the majority of existing work has concentrated on English datasets, with limited attention given to multimodal strategies in the Chinese context. In this study, we propose MMBERT, a novel BERT-based m…

Exploring AI-Enabled Test Practice, Affect, and Test Outcomes in Language Assessment
Jill Burstein, Ramsey Cardwell, Ping-Ling Chuang, Allison Michalowski, Steven Nydick
https://arxiv.org/abs/2508.17108

Exploring AI-Enabled Test Practice, Affect, and Test Outcomes in Language Assessment
Practice tests for high-stakes assessment are intended to build test familiarity, and reduce construct-irrelevant variance which can interfere with valid score interpretation. Generative AI-driven, automated item generation (AIG) scales the creation of large item banks and multiple practice tests, enabling repeated practice opportunities. We conducted a large-scale observational study (N = 25,969) using the Duolingo English Test (DET) -- a digital, high-stakes, computer-adaptive English languag…

Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Kazi Mahathir Rahman, Naveed Imtiaz Nafis, Md. Farhan Sadik, Mohammad Al Rafi, Mehedi Hasan Shahed
https://arxiv.org/abs/2507.06530

Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Helping deaf and hard-of-hearing people communicate more easily is the main goal of Automatic Sign Language Translation. Although most past research has focused on turning sign language into text, doing the reverse, turning spoken English into sign language animations, has been largely overlooked. That's because it involves multiple steps, such as understanding speech, translating it into sign-friendly grammar, and generating natural human motion. In this work, we introduce a complete pipeline …

wordnet: WordNet relationships
A network of English words from the WordNet. Node is a word, and edge denotes relationships between words (synonymy, hyperonymy, meronymy, etc.). The date at which this network was extracted from WordNet is not unknown.
This network has 146005 nodes and 656999 edges.
Tags: Informational, Language, Unweighted
htt…

wiki_users: Wikipedia user interaction (2011)
A network derived from interactions between editors of the English language Wikipedia, as derived from the edit histories of 563 wiki pages related to politics. A positive sign indicates positive links such as trust or similarities, and a negative sign indicates distrust or disagreement.
This network has 138592 nodes and 740397 edges.
Tags: Social, Online, Signed

Grants & Institutional Partnerships Manager: https://benborges.xyz/2025/08/22/grants-amp-institutional-partnerships-manager.html

Trump asks Liberian president where he learned English, his country's official language (Reuters)
https://www.reuters.com/world/africa/trump-asks-liberian-president-where-he-learned-english-his-countrys-official-2025-07-09/
http://www.memeorandum.com/250709/p109#a250709p109

Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
Jay L. Cunningham, Adinawa Adjagbodjou, Jeffrey Basoah, Jainaba Jawara, Kowe Kadoma, Aaleyah Lewis
https://arxiv.org/abs/2508.18288

Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR) and adjacent speech and language technologies (SLT) for African American English (AAE) speakers and other linguistically diverse communities. Drawing from 44 peer-reviewed publications across Human-Computer Interaction (HCI), Machine Learning/Natural Language Processing (ML/NLP), and Sociolinguistics, we identify four major areas of inquiry: (1) how…

This https://arxiv.org/abs/2505.24195 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions
With more than 11 times as many pageviews as the next, English Wikipedia dominates global knowledge access relative to other language editions. Readers are prone to assuming English Wikipedia as a superset of all language editions, leading many to prefer it even when their primary language is not English. Other language editions, however, comprise complementary facts rooted in their respective cultures and media environments, which are marginalized in English Wikipedia. While Wikipedia's user i…

BALSAM: A Platform for Benchmarking Arabic Large Language Models
Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed, Waad Alshammari, Muneera Alhoshan, Amal Almazrua, Asma Al Wazrah, Mais Alheraki, Firoj Alam, Preslav Nakov, Norah Alzahrani, Eman alBilali, Nizar Habash, Abdelrahman El-Sheikh, Muhammad Elmallah, Haonan Li, Hamdy Mubarak, Mohamed Anwar, Zaid Alyafeai, Ahmed Abdelali, Nora Altwairesh, Maram Hasanain, Abdulmohsen Al Thubaity, Shady Shehata, Bashar Alhafni, Injy Hamed, Go I…

BALSAM: A Platform for Benchmarking Arabic Large Language Models
The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data scarcity, linguistic diversity of Arabic and its dialects, morphological complexity, etc. Progress is further hindered by the quality of Arabic benchmarks, which typically rely on static, publicly available data, lack comprehensive task coverage, or do not provide dedicated platforms with blind test sets. This makes it…

wiki_users: Wikipedia user interaction (2011)
A network derived from interactions between editors of the English language Wikipedia, as derived from the edit histories of 563 wiki pages related to politics. A positive sign indicates positive links such as trust or similarities, and a negative sign indicates distrust or disagreement.
This network has 138592 nodes and 740397 edges.
Tags: Social, Online, Signed

Automatic Large Language Models Creation of Interactive Learning Lessons
Jionghao Lin, Jiarui Rao, Yiyang Zhao, Yuting Wang, Ashish Gurung, Amanda Barany, Jaclyn Ocumpaugh, Ryan S. Baker, Kenneth R. Koedinger
https://arxiv.org/abs/2506.17356

Automatic Large Language Models Creation of Interactive Learning Lessons
We explore the automatic generation of interactive, scenario-based lessons designed to train novice human tutors who teach middle school mathematics online. Employing prompt engineering through a Retrieval-Augmented Generation approach with GPT-4o, we developed a system capable of creating structured tutor training lessons. Our study generated lessons in English for three key topics: Encouraging Students' Independence, Encouraging Help-Seeking Behavior, and Turning on Cameras, using a task deco…

Vertical Video Creator: https://benborges.xyz/2025/08/20/vertical-video-creator.html

word_adjacency: Word Adjacency Networks
Directed Networks of word adjacency in texts of several languages including English, French, Spanish and Japanese.
This network has 2704 nodes and 8300 edges.
Tags: Informational, Language, Unweighted
https://networks.skewed.de/net/word_ad

HITSZ's End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track
Xuchen Wei, Yangxin Wu, Yaoyin Zhang, Henglyu Liu, Kehai Chen, Xuefeng Bai, Min Zhang
https://arxiv.org/abs/2507.19616

HITSZ's End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track
This paper presents HITSZ's submission for the IWSLT 2025 Indic track, focusing on speech-to-text translation (ST) for English-to-Indic and Indic-to-English language pairs. To enhance translation quality in this low-resource scenario, we propose an end-to-end system integrating the pre-trained Whisper automated speech recognition (ASR) model with Krutrim, an Indic-specialized large language model (LLM). Experimental results demonstrate that our end-to-end system achieved average BLEU scores of …

"How to Explore Biases in Speech Emotion AI with Users?" A Speech-Emotion-Acting Study Exploring Age and Language Biases
Josephine Beatrice Skovbo Borre, Malene Gorm Wold, Sara Kj{\ae}r Rasmussen, Ilhan Aslan
https://arxiv.org/abs/2507.12580

"How to Explore Biases in Speech Emotion AI with Users?" A Speech-Emotion-Acting Study Exploring Age and Language Biases
This study explores how age and language shape the deliberate vocal expression of emotion, addressing underexplored user groups, Teenagers (N = 12) and Adults 55+ (N = 12), within speech emotion recognition (SER). While most SER systems are trained on spontaneous, monolingual English data, our research evaluates how such models interpret intentionally performed emotional speech across age groups and languages (Danish and English). To support this, we developed a novel experimental paradigm comb…

Text2Cypher Across Languages: Evaluating Foundational Models Beyond English
Makbule Gulcin Ozsoy, William Tai
https://arxiv.org/abs/2506.21445 https://

Text2Cypher Across Languages: Evaluating Foundational Models Beyond English
Recent advances in large language models have enabled natural language interfaces that translate user questions into database queries, such as Text2SQL, Text2SPARQL, and Text2Cypher. While these interfaces enhance database accessibility, most research today focuses solely on English, with limited evaluation in other languages. This paper investigates the performance of foundational LLMs on the Text2Cypher task across multiple languages. We create and release a multilingual test set by translati…

wiki_article_words: Wikipedia article-words (en) (2010)
A bipartite network of English Wikipedia articles and the words they contain. The edge weight gives the number of times a word appeared in the connected article.
This network has 276739 nodes and 2941902 edges.
Tags: Informational, Language, Weighted
https://

BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning
Jo\~ao Guilherme Alves Santos, Giovana Kerche Bon\'as, Thales Sales Almeida
https://arxiv.org/abs/2508.21294

BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning
With the growing capabilities of Large Language Models (LLMs), there is an increasing need for robust evaluation methods, especially in multilingual and non-English contexts. We present an updated version of the BLUEX dataset, now including 2024-2025 exams and automatically generated image captions using state-of-the-art models, enhancing its relevance for data contamination studies in LLM pretraining. Captioning strategies increase accessibility to text-only models by more than 40%, producing …

Dhati : Fine-tuned Large Language Models for Arabic Subjectivity Evaluation
Slimane Bellaouar, Attia Nehar, Soumia Souffi, Mounia Bouameur
https://arxiv.org/abs/2508.19966 https…

Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation
Despite its significance, Arabic, a linguistically rich and morphologically complex language, faces the challenge of being under-resourced. The scarcity of large annotated datasets hampers the development of accurate tools for subjectivity analysis in Arabic. Recent advances in deep learning and Transformers have proven highly effective for text classification in English and French. This paper proposes a new approach for subjectivity assessment in Arabic textual data. To address the dearth of s…

wordnet: WordNet relationships
A network of English words from the WordNet. Node is a word, and edge denotes relationships between words (synonymy, hyperonymy, meronymy, etc.). The date at which this network was extracted from WordNet is not unknown.
This network has 146005 nodes and 656999 edges.
Tags: Informational, Language, Unweighted
htt…

skLEP: A Slovak General Language Understanding Benchmark
Marek \v{S}uppa, Andrej Ridzik, Daniel Hl\'adek, Tom\'a\v{s} Jav\r{u}rek, Vikt\'oria Ondrejov\'a, Krist\'ina S\'asikov\'a, Martin Tamajka, Mari\'an \v{S}imko
https://arxiv.org/abs/2506.21508

skLEP: A Slovak General Language Understanding Benchmark
In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities. To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources. Within this p…

The Impact of Language Mixing on Bilingual LLM Reasoning
Yihao Li, Jiayi Xin, Miranda Muqing Miao, Qi Long, Lyle Ungar
https://arxiv.org/abs/2507.15849 htt…

The Impact of Language Mixing on Bilingual LLM Reasoning
Proficient multilingual speakers often intentionally switch languages in the middle of a conversation. Similarly, recent reasoning-focused bilingual large language models (LLMs) with strong capabilities in both languages exhibit language mixing--alternating languages within their chain of thought. Discouraging this behavior in DeepSeek-R1 was found to degrade accuracy, suggesting that language mixing may benefit reasoning. In this work, we study language switching in Chinese-English bilingual r…

Signs of Struggle: Spotting Cognitive Distortions across Language and Register
Abhishek Kuber, Enrico Liscio, Ruixuan Zhang, Caroline Figueroa, Pradeep K. Murukannaiah
https://arxiv.org/abs/2508.20771 …

Signs of Struggle: Spotting Cognitive Distortions across Language and Register
Rising mental health issues among youth have increased interest in automated approaches for detecting early signs of psychological distress in digital text. One key focus is the identification of cognitive distortions, irrational thought patterns that have a role in aggravating mental distress. Early detection of these distortions may enable timely, low-cost interventions. While prior work has focused on English clinical data, we present the first in-depth study of cross-lingual and cross-regis…

HyperCLOVA X THINK Technical Report
NAVER Cloud HyperCLOVA X Team
https://arxiv.org/abs/2506.22403 https://arxiv.org/pdf/2506.22403…

HyperCLOVA X THINK Technical Report
We introduce HyperCLOVA X THINK, the first reasoning-focused large language model in the HyperCLOVA X family, pre-trained on roughly $6$ trillion high-quality Korean, and English tokens, augmented with targeted synthetic Korean data. It was implemented as a compute-memory-balanced Peri-LN Transformer scaled with $μ$P, pre-trained through a three-stage curriculum that expands the context window to $128$K tokens, and post-trained via supervised fine-tuning with Reinforcement Learning from Verifi…

Investigating Hallucination in Conversations for Low Resource Languages
Amit Das, Md. Najib Hasan, Souvika Sarkar, Zheng Zhang, Fatemeh Jamshidi, Tathagata Bhattacharya, Nilanjana Raychawdhury, Dongji Feng, Vinija Jain, Aman Chadha
https://arxiv.org/abs/2507.22720

Investigating Hallucination in Conversations for Low Resource Languages
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text that closely resemble human writing. However, they often generate factually incorrect statements, a problem typically referred to as 'hallucination'. Addressing hallucination is crucial for enhancing the reliability and effectiveness of LLMs. While much research has focused on hallucinations in English, our study extends this investigation to conversational data in three languages: Hindi, Farsi, and Mandari…

wiki_users: Wikipedia user interaction (2011)
A network derived from interactions between editors of the English language Wikipedia, as derived from the edit histories of 563 wiki pages related to politics. A positive sign indicates positive links such as trust or similarities, and a negative sign indicates distrust or disagreement.
This network has 138592 nodes and 740397 edges.
Tags: Social, Online, Signed

wordnet: WordNet relationships
A network of English words from the WordNet. Node is a word, and edge denotes relationships between words (synonymy, hyperonymy, meronymy, etc.). The date at which this network was extracted from WordNet is not unknown.
This network has 146005 nodes and 656999 edges.
Tags: Informational, Language, Unweighted
htt…

Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering
Julius Gun, Timo Oksanen
https://arxiv.org/abs/2508.18093 https://

Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering
We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task. Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German. It simulates a cross-lingual information retrieval scenario where questions are posed in English against all three language versions of the manual. The evaluation focuses on realistic "needle-in-a-haystack" challenges and includes unanswerable que…

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Aleksandr Perevalov, Andreas Both
https://arxiv.org/abs/2507.16971

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Accessing knowledge via multilingual natural-language interfaces is one of the emerging challenges in the field of information retrieval and related ones. Structured knowledge stored in knowledge graphs can be queried via a specific query language (e.g., SPARQL). Therefore, one needs to transform natural-language input into a query to fulfill an information need. Prior approaches mostly focused on combining components (e.g., rule-based or neural-based) that solve downstream tasks and come up wi…

wordnet: WordNet relationships
A network of English words from the WordNet. Node is a word, and edge denotes relationships between words (synonymy, hyperonymy, meronymy, etc.). The date at which this network was extracted from WordNet is not unknown.
This network has 146005 nodes and 656999 edges.
Tags: Informational, Language, Unweighted
htt…

Information availability in different languages and various technological constraints related to multilinguism on the Internet
Sonal Khosla, Haridasa Acharya
https://arxiv.org/abs/2508.17918

Information availability in different languages and various technological constraints related to multilinguism on the Internet
The usage of Internet has grown exponentially over the last two decades. The number of Internet users has grown from 16 Million to 1650 Million from 1995 to 2010. It has become a major repository of information catering almost every area. Since the Internet has its origin in USA which is English speaking country there is huge dominance of English on the World Wide Web. Although English is a globally acceptable language, still there is a huge population in the world which is not able to access t…

HebID: Detecting Social Identities in Hebrew-language Political Text
Guy Mor-Lan, Naama Rivlin-Angert, Yael R. Kaplan, Tamir Sheafer, Shaul R. Shenhav
https://arxiv.org/abs/2508.15483

HebID: Detecting Social Identities in Hebrew-language Political Text
Political language is deeply intertwined with social identities. While social identities are often shaped by specific cultural contexts and expressed through particular uses of language, existing datasets for group and identity detection are predominantly English-centric, single-label and focus on coarse identity categories. We introduce HebID, the first multilabel Hebrew corpus for social identity detection: 5,536 sentences from Israeli politicians' Facebook posts (Dec 2018-Apr 2021), manually…

The Emergence of Abstract Thought in Large Language Models Beyond Any Language
Yuxin Chen, Yiran Zhao, Yang Zhang, An Zhang, Kenji Kawaguchi, Shafiq Joty, Junnan Li, Tat-Seng Chua, Michael Qizhe Shieh, Wenxuan Zhang
https://arxiv.org/abs/2506.09890

The Emergence of Abstract Thought in Large Language Models Beyond Any Language
As large language models (LLMs) continue to advance, their capacity to function effectively across a diverse range of languages has shown marked improvement. Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts. This has led to the widespread assumption that LLMs may "think" in English. However, more recent results showing strong multilingual performance, even surpassing English performance on specific tasks in other…

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
Rana Alshaikh, Israa Alghanmi, Shelan Jeawak
https://arxiv.org/abs/2507.18442 https://…

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
The cognitive and reasoning abilities of large language models (LLMs) have enabled remarkable progress in natural language processing. However, their performance in interpreting structured data, especially in tabular formats, remains limited. Although benchmarks for English tabular data are widely available, Arabic is still underrepresented because of the limited availability of public resources and its unique language features. To address this gap, we present AraTable, a novel and comprehensiv…

ViExam: Are Vision Language Models Better than Humans on Vietnamese Multimodal Exam Questions?
Vy Tuong Dang, An Vo, Quang Tau, Duc Dm, Daeyoung Kim
https://arxiv.org/abs/2508.13680

ViExam: Are Vision Language Models Better than Humans on Vietnamese Multimodal Exam Questions?
Vision language models (VLMs) demonstrate remarkable capabilities on English multimodal tasks, but their performance on low-resource languages with genuinely multimodal educational content remains largely unexplored. In this work, we test how VLMs perform on Vietnamese educational assessments, investigating whether VLMs trained predominantly on English data can handle real-world cross-lingual multimodal reasoning. Our work presents the first comprehensive evaluation of VLM capabilities on multi…

Breaking Language Barriers: Equitable Performance in Multilingual Language Models
Tanay Nagar, Grigorii Khvatskii, Anna Sokol, Nitesh V. Chawla
https://arxiv.org/abs/2508.12662 …

Breaking Language Barriers: Equitable Performance in Multilingual Language Models
Cutting-edge LLMs have emerged as powerful tools for multilingual communication and understanding. However, LLMs perform worse in Common Sense Reasoning (CSR) tasks when prompted in low-resource languages (LRLs) like Hindi or Swahili compared to high-resource languages (HRLs) like English. Equalizing this inconsistent access to quality LLM outputs is crucial to ensure fairness for speakers of LRLs and across diverse linguistic communities. In this paper, we propose an approach to bridge this ga…

Multilingual Self-Taught Faithfulness Evaluators
Carlo Alfano, Aymen Al Marjani, Zeno Jonke, Amin Mantrach, Saab Mansour, Marcello Federico
https://arxiv.org/abs/2507.20752 http…

Multilingual Self-Taught Faithfulness Evaluators
The growing use of large language models (LLMs) has increased the need for automatic evaluation systems, particularly to address the challenge of information hallucination. Although existing faithfulness evaluation approaches have shown promise, they are predominantly English-focused and often require expensive human-labeled training data for fine-tuning specialized models. As LLMs see increased adoption in multilingual contexts, there is a need for accurate faithfulness evaluators that can ope…

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity...
Jay L. Cunningham, Adinawa Adjagbodjou, Jeffrey Basoah, Jainaba Jawara, Kowe Kadoma, Aaleyah Lewis

Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis
Anusha Kamath, Kanishk Singla, Rakesh Paul, Raviraj Joshi, Utkarsh Vaidya, Sanjay Singh Chauhan, Niranjan Wartikar
https://arxiv.org/abs/2508.19831

Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis
Evaluating instruction-tuned Large Language Models (LLMs) in Hindi is challenging due to a lack of high-quality benchmarks, as direct translation of English datasets fails to capture crucial linguistic and cultural nuances. To address this, we introduce a suite of five Hindi LLM evaluation datasets: IFEval-Hi, MT-Bench-Hi, GSM8K-Hi, ChatRAG-Hi, and BFCL-Hi. These were created using a methodology that combines from-scratch human annotation with a translate-and-verify process. We leverage this su…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages
Baban Gain, Dibyanayan Bandyopadhyay, Samrat Mukherjee, Chandranath Adak, Asif Ekbal

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection
Axel Delaval, Shujian Yang, Haicheng Wang, Han Qiu, Jialiang Lu
https://arxiv.org/abs/2508.11281

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection
Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in French remains underdeveloped, primarily due to the lack of culturally relevant, large-scale datasets. In this work, we introduce TOXIFRENCH, a new public benchmark of 53,622 French online comments, constructed via a semi-automated annotation pipeline that reduces manual labeling to only 10% through high-confidence LLM-based pre-annotation and huma…

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation
Leah von der Heyde, Anna-Carolina Haensch, Bernd Wei{\ss}, Jessika Daikeler
https://arxiv.org/abs/2506.14634

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation
The recent development and wider accessibility of LLMs have spurred discussions about how they can be used in survey research, including classifying open-ended survey responses. Due to their linguistic capacities, it is possible that LLMs are an efficient alternative to time-consuming manual coding and the pre-training of supervised machine learning models. As most existing research on this topic has focused on English-language responses relating to non-complex topics or on single LLMs, it is u…

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Ammar Khairi, Daniel D'souza, Ye Shen, Julia Kreutzer, Sara Hooker
https://arxiv.org/abs/2506.20544

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute, improving performance without retraining the model. A common approach is to sample multiple outputs in parallel, and select one of these as the final output. However, work to date has focused on English and a handful of domains such as math and code. In contrast, we are most interested in techniques that generalize across open-ended tasks, formally verifiable tasks, and across languages…

HATS: Hindi Analogy Test Set for Evaluating Reasoning in Large Language Models
Ashray Gupta, Rohan Joseph, Sunny Rai
https://arxiv.org/abs/2507.13238 https…

HATS: Hindi Analogy Test Set for Evaluating Reasoning in Large Language Models
Analogies test a model's ability to infer implicit relationships between concepts, making them a key benchmark for evaluating reasoning capabilities. While large language models (LLMs) are widely evaluated for reasoning in English, their abilities in Indic languages remain understudied, limiting our understanding of whether these models generalize across languages. To address this gap, we introduce a new Hindi Analogy Test Set (HATS), comprising 405 multiple-choice questions sourced from Indian…

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages
Imalsha Puranegedara, Themira Chathumina, Nisal Ranathunga, Nisansa de Silva, Surangika Ranathunga, Mokanarangan Thayaparan
https://arxiv.org/abs/2508.09091

Utilizing Multilingual Encoders to Improve Large Language Models for Low-Resource Languages
Large Language Models (LLMs) excel in English, but their performance degrades significantly on low-resource languages (LRLs) due to English-centric training. While methods like LangBridge align LLMs with multilingual encoders such as the Massively Multilingual Text-to-Text Transfer Transformer (mT5), they typically use only the final encoder layer. We propose a novel architecture that fuses all intermediate layers, enriching the linguistic information passed to the LLM. Our approach features tw…

Principle Methods of Rendering Non-equivalent Words from Uzbek and Dari to Russian and English
Mohammad Ibrahim Qani
https://arxiv.org/abs/2508.15453 https://

Principle Methods of Rendering Non-equivalent Words from Uzbek and Dari to Russian and English
These pure languages understanding directly relates to translation knowledge where linguists and translators need to work and research to eradicate misunderstanding. Misunderstandings mostly appear in non-equivalent words because there are different local and internal words like food, garment, cultural and traditional words and others in every notion. Truly, most of these words do not have equivalent in the target language and these words need to be worked and find their equivalent in the targe…

Reveal-Bangla: A Dataset for Cross-Lingual Multi-Step Reasoning Evaluation
Khondoker Ittehadul Islam, Gabriele Sarti
https://arxiv.org/abs/2508.08933 https://

Reveal-Bangla: A Dataset for Cross-Lingual Multi-Step Reasoning Evaluation
Language models have demonstrated remarkable performance on complex multi-step reasoning tasks. However, their evaluation has been predominantly confined to high-resource languages such as English. In this paper, we introduce a manually translated Bangla multi-step reasoning dataset derived from the English Reveal dataset, featuring both binary and non-binary question types. We conduct a controlled evaluation of English-centric and Bangla-centric multilingual small language models on the origin…

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong
Sirui Han, Junqi Zhu, Ruiyuan Zhang, Yike Guo
https://arxiv.org/abs/2507.11502 http…

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong
This paper presents the development of HKGAI-V1, a foundational sovereign large language model (LLM), developed as part of an initiative to establish value-aligned AI infrastructure specifically tailored for Hong Kong. Addressing the region's unique multilingual environment (Cantonese, Mandarin, and English), its distinct socio-legal context under the "one country, two systems" framework, and specific local cultural and value considerations, the model is built upon the DeepSeek architecture and…