Tootfinder

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…

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://

I'm a #language fiend too. Lasswell taps into a big sore point for me as well. If you read this, stay away from the comment section. You'll go nuts!
Opinion | The phrase ‘begs the question’ is begging for oblivion - The Washington Post

This common phrase is widely misused. Is it worth salvaging?
When a phrase has been misused into uselessness, maybe avoiding it is the best policy.

SWELL (old-timey use) Vs SWILL. What a difference a letter makes.
#English #language #difference

«Sure, people from Latin-language-majority countries also list their “pronouns” in e-mail signatures and on social-media profiles, but this is, I think, largely the subconscious acceptance of the English-speakers’ hegemony over social norms (and queerness?).»
🔥 by @….
I'd even go one further and say "USian English's hegemony, both for online social norms generally and queerness in particular 🙈
https://blog.achintyarao.in/post/the-english-language/

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…

I'm a #language fiend too. Lasswell taps into a big sore point for me as well. If you read this, stay away from the comment section. You'll go nuts!
Opinion | The phrase ‘begs the question’ is begging for oblivion - The Washington Post

This common phrase is widely misused. Is it worth salvaging?
When a phrase has been misused into uselessness, maybe avoiding it is the best policy.

The Belfast News Letter, among the longest-running English dailies, digitizes and puts all its editions online, including the earliest surviving one from 1738 (Michael Savage/The Guardian)
https://www.theguardian.com/media/2025/jun/…

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…

WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions
Zining Wang, Yuxuan Zhang, Dongwook Yoon, Nicholas Vincent, Farhan Samir, Vered Shwartz
https://arxiv.org/abs/2505.24195

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…

At the Semantic Digital Humanities 2025 Workshop, Jose Maldonado-Rodríguez is presenting "Natural Language Querying for Humanities #KnowledgeGraphs A case study on the GOLEM KG". Main contribution is a bilingual dataset (English-Spanish) specifically designed to evaluate automatic text-to-SPARQL translation systems for GOLEM, a specialized humanities KG.
paper:

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.

An Exploratory Framework for Future SETI Applications: Detecting Generative Reactivity via Language Models
Po-Chieh Yu
#toXiv_bot_toot

Title: The End of Anarchism?
Author: Luigi Galleani
Topics: #AnarchoCommunism, #insurrectionary
Date: 1925
Link:

Watching the first Question Period of the 45th Parliament of Canada!
#CanPoli #CdnPoli

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…

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…

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…

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, …

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

Stop the decline!
Marked decline in semicolons in English books, study suggests | Language | The Guardian
https://www.theguardian.com/science/2025/may/18/marked-decline-semicolon-use-english-books-study-suggests

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 …

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…

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

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…

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…

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. …

A new best-speller
https://apnews.com/article/scripps-national-spelling-bee-2025-champion-70f6767e4f30a29b52dfc3dfc77eb553

Faizan Zaki wins the Scripps National Spelling Bee
Faizan Zaki has seized the title of best speller in the English language at the Scripps National Spelling Bee. The 13-year-old from Allen, Texas, was runner-up last year after he lost in a lightning-round tiebreaker. He ousted eight other accomplished spellers to win the title on Thursday night, including two that he let back into the competition after his own careless flub. Told to take a deep breath before his final word, “eclaircissement,” he didn’t ask a single question before spellin…

Mixing up the two Swedish words "Godkänna" and "Gödkanna" in a project may seem like an innocent mistake, especially if you only speak English. However, they mean very different things... Read more to learn what.
#humor #language

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…

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

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 41424 nodes and 73900 edges.
Tags: Social, Communication, Unweighted, Multigraph,…

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…

It bothers me big time that many Reddit threads are auto-translated to my native language and indexed in search engines.
As someone who frequently searches for highly local knowledge, I find this annoying.
If I search anything in my native language, it usually means "I want to know what my fellows think", and not "I want to read what English-speaking community says, just translated to Polish".
Outside of a few specific exceptions, Reddit in general is such a waste of time and data.

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 …

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...

Of all the many crimes against the English language committed by people from the USA, I think the only one which I'd make a capital offence is that of refusing to pronounce the "L" in "soldering".

Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi
Arnav Rustagi, Satvik Bajpai, Nimrat Kaur, Siddharth Siddharth
https://arxiv.org/abs/2506.02166

Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi
Computer-Assisted Pronunciation Training (CAPT) has been extensively studied for English. However, there remains a critical gap in its application to Indian languages with a base of 1.5 billion speakers. Pronunciation tools tailored to Indian languages are strikingly lacking despite the fact that millions learn them every year. With over 600 million speakers and being the fourth most-spoken language worldwide, improving Hindi pronunciation is a vital first step toward addressing this gap. This …

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…

I don't know why I'm learning to say "right" and "left" in another language when I can't even keep them straight in English.

Day 13 (oh, really? ;))
Registration Form Implementation
I've just finished implementing a registration form with validation and language switching using Next.js and React Hook Form. Now users can register with dynamic language support (English/Polish) and data validation (email, password, phone).
Unfortunately, my account on Write.as has been temporarily blocked, so details about the implementation will be available once the account is unlocked. Stay tuned! 😊

Mixing up the two Swedish words "Godkänna" and "Gödkanna" in a project may seem like an innocent mistake, especially if you only speak English. However, they mean very different things... Read more to learn what.
#humor #language

I got to use `progenitor` in an uncontrived context at work today, and last week it was `prevaricate`, so if you need any fancy English vocabulary needs I'm right over here, under the sign `Fancy Language Person`.

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…

temporarily pasting something into MS Word and the Editor pops up with this real helpful tip 📎

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://

I'm Linuxing my pizza 🙂
#linux #pizza

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…

GLAP: General contrastive audio-text pretraining across domains and languages
Heinrich Dinkel, Zhiyong Yan, Tianzi Wang, Yongqing Wang, Xingwei Sun, Yadong Niu, Jizhong Liu, Gang Li, Junbo Zhang, Jian Luan
https://arxiv.org/abs/2506.11350

GLAP: General contrastive audio-text pretraining across domains and languages
Contrastive Language Audio Pretraining (CLAP) is a widely-used method to bridge the gap between audio and text domains. Current CLAP methods enable sound and music retrieval in English, ignoring multilingual spoken content. To address this, we introduce general language audio pretraining (GLAP), which expands CLAP with multilingual and multi-domain abilities. GLAP demonstrates its versatility by achieving competitive performance on standard audio-text retrieval benchmarks like Clotho and AudioC…

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…

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…

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…

This https://arxiv.org/abs/2505.05660 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs
As large language models (LLMs) increasingly adapt and personalize to diverse sets of users, there is an increased risk of systems appropriating sociolects, i.e., language styles or dialects that are associated with specific minoritized lived experiences (e.g., African American English, Queer slang). In this work, we examine whether sociolect usage by an LLM agent affects user reliance on its outputs and user perception (satisfaction, frustration, trust, and social presence). We designed and co…

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…

Replaced article(s) found for q-bio.OT. https://arxiv.org/list/q-bio.OT/new
[1/1]:
English dictionaries, gold and silver standard corpora for biomedical natural language processing...

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 155820 nodes and 1358426 edges.
Tags: Social, Communication, Unweighted, Multigra…

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…

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students
Emma R. Dodoo, Tamara Nelson-Fromm, Mark Guzdial
https://arxiv.org/abs/2506.14147

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students
K-12 computing teachers must navigate complex trade-offs when selecting programming languages and instructional materials for classrooms with emergent bilingual students. While they aim to foster an inclusive learning environment by addressing language barriers that impact student engagement, they must also align with K-12 computer science curricular guidelines and prepare students for industry-standard programming tools. Because programming languages predominantly use English keywords and most…

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

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

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

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…

Intelligibility of Text-to-Speech Systems for Mathematical Expressions
Sujoy Roychowdhury, H. G. Ranjani, Sumit Soman, Nishtha Paul, Subhadip Bandyopadhyay, Siddhanth Iyengar
https://arxiv.org/abs/2506.11086

Intelligibility of Text-to-Speech Systems for Mathematical Expressions
There has been limited evaluation of advanced Text-to-Speech (TTS) models with Mathematical eXpressions (MX) as inputs. In this work, we design experiments to evaluate quality and intelligibility of five TTS models through listening and transcribing tests for various categories of MX. We use two Large Language Models (LLMs) to generate English pronunciation from LaTeX MX as TTS models cannot process LaTeX directly. We use Mean Opinion Score from user ratings and quantify intelligibility through…

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…

This https://arxiv.org/abs/2506.00759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons
Large Language Models (LLMs) trained on massive data capture rich information embedded in the training data. However, this also introduces the risk of privacy leakage, particularly involving personally identifiable information (PII). Although previous studies have shown that this risk can be mitigated through methods such as privacy neurons, they all assume that both the (sensitive) training data and user queries are in English. We show that they cannot defend against the privacy leakage in cro…