ymoslem/acl-abstracts huggingface dataset:
Drawing Blue Lines - What can Constraint Grammar do for GEC? (2025)

This paper presents the application of rule-based methods for Grammatical Error Correction (GEC) across multiple low-resource languages. We describe new functionality using the Constraint Grammar (CG) formalism, designed for detecting and correcting different types of complex grammatical errors in a range of morphologically complex languages. These errors require transformations such as reordering, word additions/deletions, and alternative choices for multiword suggestions. New perspectives are gained from end-to-end-testing {--} this work aims to clarify the relationship between the command-line interface used by developers and the user interfaces of our grammar checker plug-in for common word processors. We discuss challenges and solutions in correcting complex errors, with examples from languages like Lule S{\'a}mi, Irish, and Greenlandic, enabling linguists to adapt these methods in order to provide accurate and context-aware proofing tools for their own languages in mainstream word processors like Microsoft Word, Google Docs or LibreOffice.
Towards Natural Language Explanations of Constraint Grammar Rules (2025)

This paper presents a general-purpose parser for static analysis of Constraint Grammar rules (that is, examining only the rules, not potential inputs and outputs) and applies it to the task of translating rules into comprehensible explanations of behavior. An interactive interface for exploring how individual components of each rule contribute to these translations is also presented.
A Mansi FST and spellchecker (2025)

The article presents a finite state transducer and spellchecker for Mansi, an Ob-Ugric Uralic language spoken in northwestern Siberia. Mansi has a rich but mostly agglutinative morphology, with a morphophonology dominated by sandhi phenomena. With a small set of morphophonological rules (32 twolc rules) and a lexicon consisting of 12,000 Mansi entries and a larger set of propernouns we were able to build a transducer covering 98.9 {\%} of a large (700k) newspaper corpus. Being a part of the GiellaLT infrastructure, the transducer was turned into a spellchecker. The most common spelling error in Mansi is the omission of length marks on vowels, and for the 1000 most common words containing long vowels, the spellchecker was able to give a correct suggestion as top-five in 98.3 {\%} of the cases, and as first suggestion in 91.3 {\%} of the cases.
A grammatical analyser for Tokelau (2025)

This article will present a grammatical aunalyser, disambiguator and dependency analysis of Tokelau. The grammatical analyser is written as a finite-state transducer (FST), whereas the disambiguator and dependency analyser are written in Constraint Grammar (CG), both within the GiellaLT infrastructure. Contrary to most languages analyzed within this framework, Being a Polynesian language, Tokelau is a predominantly isolating language, with reduplication and affixation as the main morphological processes. The article will discuss how FST and CG deal with Polynesian languages.
A Grammar-Based Method for Instilling Empirical Dependency Structure in LLMs (2025)

We investigate whether synthetic pretraining data generated from a formal grammar modeling syntactic dependencies can improve English language models. Building upon the structured pretraining data approach of Papadimitriou and Jurafsky (2023), we develop a grammar that more closely mirrors empirical dependency structures. Our results are negative {--} this type of pretraining significantly degrades model performance, with both our and their pretraining approach performing worse than no pretraining at all. We analyze potential explanations for these findings and discuss implications for future work on structured-data pretraining.
Case error corrections for noun phrases containing deverbal attributive nouns in Greenlandic (2025)

This paper contains preliminary findings using Constraint Grammar (CG) in seman- tic annotation in a specific type of noun phrases in Greenlandic, in which the at- tributive noun is a nominalized predica- tive verbal stem. The annotation is used in a grammar checker pipeline for the pur- pose of making case error correction sug- gestions.
Divvunspell---Finite-State Spell-Checking and Correction on Modern Platforms (2025)

Spell-checking and correction is one of the key applications of natural language support. Historically, for the biggest, less morphologically complex languages, spell-checking and correction could be implemented by relatively simple means; however, for morphologically complex and low-resource languages, the solutions were often suboptimal. Finite-state methods are the state of the art in rule-based natural language processing and also for spell-checking and correction they have been effectively used. In this article, we show some recent developments of a finite-state spell-checker implementation that works with modern operating systems and platforms.
Euska\~nolDS: A Naturally Sourced Corpus for Basque-Spanish Code-Switching (2025)

Code-switching (CS) remains a significant challenge in Natural Language Processing (NLP), mainly due a lack of relevant data. In the context of the contact between the Basque and Spanish languages in the north of the Iberian Peninsula, CS frequently occurs in both formal and informal spontaneous interactions. However, resources to analyse this phenomenon and support the development and evaluation of models capable of understanding and generating code-switched language for this language pair are almost non-existent. We introduce a first approach to develop a naturally sourced corpus for Basque-Spanish code-switching. Our methodology consists of identifying CS texts from previously available corpora using language identification models, which are then manually validated to obtain a reliable subset of CS instances. We present the properties of our corpus and make it available under the name Euska{\~n}olDS.
The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR (2025)

Code-switching, the act of alternating between languages, emerged as a prevalent global phenomenon that needs to be addressed for building user-friendly language technologies. A main bottleneck in this pursuit is data scarcity, motivating research in the direction of code-switched data augmentation. However, current literature lacks comprehensive studies that enable us to understand the relation between the quality of synthetic data and improvements on NLP tasks. We extend previous research conducted in this direction on machine translation (MT) with results on automatic speech recognition (ASR) and cascaded speech translation (ST) to test generalizability of findings. Our experiments involve a wide range of augmentation techniques, covering lexical replacements, linguistic theories, and back-translation. Based on the results of MT, ASR, and ST, we draw conclusions and insights regarding the efficacy of various augmentation techniques and the impact of quality on performance.
Beyond Monolingual Limits: Fine-Tuning Monolingual ASR for Yoruba-English Code-Switching (2025)

Code-switching (CS) presents a significant challenge for Automatic Speech Recognition (ASR) systems, particularly in low-resource settings. While multilingual ASR models like OpenAI Whisper Large v3 are designed to handle multiple languages, their high computational demands make them less practical for real-world deployment in resource-constrained environments. In this study, we investigate the effectiveness of fine-tuning both monolingual and multilingual ASR models for Yoruba-English CS speech. Our results show that unadapted monolingual ASR models outperform Whisper Large v3 in a zero-shot setting on CS speech. Fine-tuning significantly reduces WER for both monolingual and multilingual models, with monolingual models achieving over a 20{\%} WER reduction on CS and Yoruba speech while maintaining lower computational costs. However, we observe a trade-off, as fine-tuning leads to some degradation in English recognition, particularly for multilingual models. Our findings highlight that while multilingual models benefit from fine-tuning, monolingual models provide a computationally efficient and competitive alternative for CS-ASR, making them a viable choice for resource-constrained environments.
Where and How Do Languages Mix? A Study of Spanish-Guaran\'i Code-Switching in Paraguay (2025)

Code-switching, the alternating use of multiple languages within a single utterance, is a widespread linguistic phenomenon that poses unique challenges for both sociolinguistic analysis and Natural Language Processing (NLP). While prior research has explored code-switching from either a syntactic or geographic perspective, few studies have integrated both aspects, particularly for underexplored language pairs like Spanish-Guaran{\'i}. In this paper, we analyze Spanish-Guaran{\'i} code-switching using a dataset of geotagged tweets from Asunci{\'o}n, Paraguay, collected from 2017 to 2021. We employ a differential distribution method to map the geographic distribution of code-switching across urban zones and analyze its syntactic positioning within sentences. Our findings reveal distinct spatial patterns, with Guaran{\'i}-dominant tweets concentrated in the western and southwestern areas, while Spanish-only tweets are more prevalent in central and eastern regions. Syntactic analysis shows that code-switching occurs most frequently in the middle of sentences, often involving verbs, pronouns, and adjectives. These results provide new insights into the interaction between linguistic, social, and geographic factors in bilingual communication. Our study contributes to both sociolinguistic research and NLP applications, offering a framework for analyzing mixed-language data in digital communication.
Tongue-Tied: Breaking LLMs Safety Through New Language Learning (2025)

The safety mechanisms of large language models (LLMs) have been shown to be fragile, as attackers can exploit prompts to generate harmful responses. Low-cost jailbreak attacks, such as those utilizing low-resource languages and code-switching, demonstrate that LLM safety mechanisms are vulnerable to low-resource languages. This indicates that safety training is particularly ineffective in low-resource languages. Furthermore, research has shown that fine-tuning LLMs with a small number of adversarial samples can compromise their safety training, implying that safety mechanism objectives can be overridden with the latest fine-tuning objectives. Based on the aforementioned statements, we hypothesize that the safety training of LLMs is language-dependent, and LLMs can potentially be compromised by fine-tuning them with new languages, even when using only harmless data.In this work, we used the low-resource language Newari and created two fake languages to LoRA-finetune LLMs with non-harmful data. Our results show that simply fine-tuning LLMs with new languages, even without the presence of harmful data, will jailbreak LLMs. Furthermore, we demonstrate that as we introduce English-to-and-from new language translation pairs in the training dataset, the attack success rate increases with harmful responses becoming more coherent. Additionally, we show the transferability of the attack by jailbreaking GPT-4 through finetuning with only 4,000 data points, and demonstrate that higher-capability models such as Claude-3.5-Sonnet can be compelled to learn to write in new languages through few-shot examples from in-context learning and can be jailbroken with new languages without fine-tuning. We furthermore investigate the fine-tuned LLMs' latents with logit lens and find that the new language fine-tuning weakens safety mechanisms by prioritizing new language fidelity over alignment, enabling jailbreaks via late-layer pivots to new language tokens that bypass English-centric safeguards. We have publicly released our trained model weights, dataset, and artifacts at this URL: https://github.com/UNHSAILLab/tongue-tied-breaking-llms-safety-through-new-language-learning
LexiLogic@CALCS 2025: Predicting Preferences in Generated Code-Switched Text (2025)

Code-switched generation is an emerging application in NLP systems, as code-switched text and speech are common and natural forms of conversation in multilingual communities worldwide. While monolingual generation has matured significantly with advances in large language models, code-switched generation still remains challenging, especially for languages and domains with less representation in pre-training datasets. In this paper, we describe our submission to the shared task of predicting human preferences for code-switched text in English-Malayalam, English-Tamil, and English-Hindi. We discuss our various approaches and report on the accuracy scores for each approach.
LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones (2025)

Large Language Models (LLMs) have the potential of being a useful tool that can automate tasks, and assist humans. However, these models are more fluent in English and more aligned with Western cultures, norms, and values. Arabic-specific LLMs are being developed to better capture the nuances of the Arabic language, and the views of the Arabs. However, Arabs are sometimes assumed to share the same culture. In this position paper, we discuss the limitations of this assumption and provide our recommendations for how to curate better alignment data that models the cultural diversity within the Arab world.
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries (2025)

Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to ensure equitable representation across different social groups, leading to biased outputs. In this paper, we introduce two novel methods for fair extractive summarization: FairExtract, a clustering-based approach, and FairGPT, which leverages GPT-3.5-turbo with fairness constraints. We evaluate these methods using Divsumm summarization dataset of White-aligned, Hispanic, and African-American dialect tweets and compare them against relevant baselines. The results obtained using a comprehensive set of summarization quality metrics such as SUPERT, BLANC, SummaQA, BARTScore, and UniEval, as well as a fairness metric F, demonstrate that FairExtract and FairGPT achieve superior fairness while maintaining competitive summarization quality. Additionally, we introduce composite metrics (e.g., SUPERT+F, BLANC+F) that integrate quality and fairness into a single evaluation framework, offering a more nuanced understanding of the trade-offs between these objectives. Our code is available online.
InspAIred: Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data (2025)

Inspiration is linked to various positive outcomes, such as increased creativity, productivity, and happiness. Although inspiration has great potential, there has been limited effort toward identifying content that is inspiring, as opposed to just engaging or positive. Additionally, most research has concentrated on Western data, with little attention paid to other cultures. This work is the first to study cross-cultural inspiration through machine learning methods. We aim to identify and analyze real and AI-generated cross-cultural inspiring posts. To this end, we compile and make publicly available the InspAIred dataset, which consists of 2,000 real inspiring posts, 2,000 real non-inspiring posts, and 2,000 generated inspiring posts evenly distributed across India and the UK. The real posts are sourced from Reddit, while the generated posts are created using the GPT-4 model. Using this dataset, we conduct extensive computational linguistic analyses to (1) compare inspiring content across cultures, (2) compare AI-generated inspiring posts to real inspiring posts, and (3) determine if detection models can accurately distinguish between inspiring content across cultures and data sources.
DaKultur: Evaluating the Cultural Awareness of Language Models for Danish with Native Speakers (2025)

Large Language Models (LLMs) have seen widespread societal adoption. However, while they are able to interact with users in languages beyond English, they have been shown to lack cultural awareness, providing anglocentric or inappropriate responses for underrepresented language communities. To investigate this gap and disentangle linguistic versus cultural proficiency, we conduct the first cultural evaluation study for the mid-resource language of Danish, in which native speakers prompt different models to solve tasks requiring cultural awareness. Our analysis of the resulting 1,038 interactions from 63 demographically diverse participants highlights open challenges to cultural adaptation: Particularly, how currently employed automatically translated data are insufficient to train or measure cultural adaptation, and how training on native-speaker data can more than double response acceptance rates. We release our study data as DaKultur - the first native Danish cultural awareness dataset.
Korean Stereotype Content Model: Translating Stereotypes Across Cultures (2025)

To address bias in language models, researchers are leveraging established social psychology research on stereotyping. This interdisciplinary approach uses frameworks like the Stereotype Content Model (SCM) to understand how stereotypes about social groups are formed and perpetuated. The SCM posits that stereotypes are based on two dimensions: warmth (intent to harm) and competence (ability to harm). This framework has been applied in NLP for various tasks, including stereotype identification, bias mitigation, and hate speech detection. While the SCM has been extensively studied in English language models and Western cultural contexts, its applicability as a cross-cultural measure of stereotypes remains an open research question. This paper explores the cross-cultural validity of the SCM by developing a Korean Stereotype Content Model (KoSCM). We create a Korean warmth-competence lexicon through machine translation of existing English lexicons, validated by an expert translator, and utilize this lexicon to develop a labeled training dataset of Korean sentences. This work presents the first extension of SCM lexicons to a non-English language (Korean), aiming to broaden understanding of stereotypes and cultural dynamics.
LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation (2025)

Content moderation platforms concentrate resources on English content despite serving predominantly non-English speaking users.Also, given the scarcity of native moderators for low-resource languages, non-native moderators must bridge this gap in moderation tasks such as hate speech moderation.Through a user study, we identify that non-native moderators struggle with understanding culturally-specific knowledge, sentiment, and internet culture in the hate speech.To assist non-native moderators, we present LLM-C3MOD, a human-LLM collaborative pipeline with three steps: (1) RAG-enhanced cultural context annotations; (2) initial LLM-based moderation; and (3) targeted human moderation for cases lacking LLM consensus.Evaluated on Korean hate speech dataset with Indonesian and German participants, our system achieves 78{\%} accuracy (surpassing GPT-4o`s 71{\%} baseline) while reducing human workload by 83.6{\%}.In addition, cultural context annotations improved non-native moderator accuracy from 22{\%} to 61{\%}, with humans notably excelling at nuanced tasks where LLMs struggle.Our findings demonstrate that non-native moderators, when properly supported by LLMs, can effectively contribute to cross-cultural hate speech moderation.
One world, one opinion? The superstar effect in LLM responses (2025)

As large language models (LLMs) are shaping the way information is shared and accessed online, their opinions have the potential to influence a wide audience. This study examines who is predicted by the studied LLMs as the most prominent figures across various fields, while using prompts in ten different languages to explore the influence of linguistic diversity. Our findings reveal low diversity in responses, with a small number of figures dominating recognition across languages (also known as the {\textquotedblleft}superstar effect{\textquotedblright}). These results highlight the risk of narrowing global knowledge representation when LLMs are used to retrieve subjective information.
Towards Region-aware Bias Evaluation Metrics (2025)

When exposed to human-generated data, language models are known to learn and amplify societal biases. While previous works introduced metrics that can be used to assess the bias in these models, they rely on assumptions that may not be universally true. For instance, a gender bias dimension commonly used by these metrics is that of family{--}career, but this may not be the only common bias in certain regions of the world. In this paper, we identify topical differences in gender bias across different regions and propose a region-aware bottom-up approach for bias assessment. Several of our proposed region-aware gender bias dimensions are found to be aligned with the human perception of gender biases in these regions.
Cross-Cultural Differences in Mental Health Expressions on Social Media (2025)

Culture moderates the way individuals perceive and express mental distress. Current understandings of mental health expressions on social media, however, are predominantly derived from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts. To address this gap, we examine mental health posts on Reddit made by individuals geolocated in India, to identify variations in social media language specific to the Indian context compared to users from Western nations. Our experiments reveal significant psychosocial variations in emotions and temporal orientation. This study demonstrates the potential of social media platforms for identifying cross-cultural differences in mental health expressions (e.g. seeking advice in India vs seeking support by Western users). Significant linguistic variations in online mental health-related language emphasize the importance of developing precision-targeted interventions that are culturally appropriate.
WHEN TOM EATS KIMCHI: Evaluating Cultural Awareness of Multimodal Large Language Models in Cultural Mixture Contexts (2025)

In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs.For example, a model should correctly identify kimchi (Korean food) in an image both when an Asian woman is eating it, as well as an African man is eating it.However, current MLLMs show an over-reliance on the visual features of the person, leading to misclassification of the entities. To examine the robustness of MLLMs to different ethnicity, we introduce MIXCUBE, a cross-cultural bias benchmark, and study elements from five countries and four ethnicities. Our findings reveal that MLLMs achieve both higher accuracy and lower sensitivity to such perturbation for high-resource cultures, but not for low-resource cultures. GPT-4o, the best-performing model overall, shows up to 58{\%} difference in accuracy between the original and perturbed cultural settings in low-resource cultures