ymoslem/acl-abstracts huggingface dataset:
PersianMCQ-Instruct: A Comprehensive Resource for Generating Multiple-Choice Questions in Persian (2025)

We present PersianMCQ-Instruct, a comprehensive resource that includes a dataset and advanced models for generating multiple-choice questions (MCQs) in standard Iranian Persian, a low-resource language spoken by over 80 million people. This resource features three state-of-the-art models for Persian MCQ generation: PMCQ-Gemma2-9b, PMCQ-Llama3.1-8b, and PMCQ-Mistral-7B. Inspired by the Agent Instruct framework and GPT-4o, we created the dataset by curating over 4,000 unique Persian Wikipedia pages, resulting in three MCQs per page and a total of over 12,000 questions. To ensure the quality of this dataset, we conducted human evaluations and model fine-tuning, both of which demonstrated significant performance improvements in Persian MCQ generation. The dataset and models are publicly available, offering valuable tools for researchers and educators, with particular benefits for advancing Persian-language educational technology.
Stop Jostling: Adaptive Negative Sampling Reduces the Marginalization of Low-Resource Language Tokens by Cross-Entropy Loss (2025)

Neural language models often struggle with low-resource languages due to the limited availability of training data, making tokens from these languages rare in the training set. This paper addresses a specific challenge during training: rare tokens are disproportionately affected by marginalization, which prevents them from learning effectively. We propose a thresholding technique that reduces the impact of this marginalization, allowing rare tokens to benefit from more meaningful alignment. Through experiments with a character-level language model, we demonstrate that this method significantly improves performance on low-resource language validation data. This work is the first to show how negative sampling can be applied to improve the representation of rare tokens by limiting the harmful influence of excessive marginalization, offering a new approach to enhancing language model performance for underrepresented languages.
Towards Inclusive Arabic LLMs: A Culturally Aligned Benchmark in Arabic Large Language Model Evaluation (2025)

Arabic Large Language Models are usually evaluated using Western-centric benchmarks that overlook essential cultural contexts, making them less effective and culturally misaligned for Arabic-speaking communities. This study addresses this gap by evaluating the Arabic Massive Multitask Language Understanding (MMLU) Benchmark to assess its cultural alignment and relevance for Arabic Large Language Models (LLMs) across culturally sensitive topics. A team of eleven experts annotated over 2,500 questions, evaluating them based on fluency, adequacy, cultural appropriateness, bias detection, religious sensitivity, and adherence to social norms. Through human assessment, the study highlights significant cultural misalignments and biases, particularly in sensitive areas like religion and morality. In response to these findings, we propose annotation guidelines and integrate culturally enriched data sources to enhance the benchmark`s reliability and relevance. The research highlights the importance of cultural sensitivity in evaluating inclusive Arabic LLMs, fostering more widely accepted LLMs for Arabic-speaking communities.
Controlled Evaluation of Syntactic Knowledge in Multilingual Language Models (2025)

Language models (LMs) are capable of acquiring elements of human-like syntactic knowledge. Targeted syntactic evaluation tests have been employed to measure how well they form generalizations about syntactic phenomena in high-resource languages such as English. However, we still lack a thorough understanding of LMs' capacity for syntactic generalizations in low-resource languages, which are responsible for much of the diversity of syntactic patterns worldwide. In this study, we develop targeted syntactic evaluation tests for three low-resource languages (Basque, Hindi, and Swahili) and use them to evaluate five families of open-access multilingual Transformer LMs. We find that some syntactic tasks prove relatively easy for LMs while others (agreement in sentences containing indirect objects in Basque, agreement across a prepositional phrase in Swahili) are challenging. We additionally uncover issues with publicly available Transformers, including a bias toward the habitual aspect in Hindi in multilingual BERT and underperformance compared to similar-sized models in XGLM-4.5B.
Evaluating Large Language Models for In-Context Learning of Linguistic Patterns In Unseen Low Resource Languages (2025)

This paper investigates the ability of Large language Models (LLMs) in capturing linguistic patterns from unseen languages and applying them to translation between the languages and English within an in-context learning framework. Inspired by the International Linguistics Olympiad (IOL), we create test data consisting of translation puzzles between 40 low resource languages and English. We test the LLMs in two different strategies: direct prompting and step-by-step prompting. In the latter, the puzzles are manually decomposed into intermediate steps to allow LLMs learn and apply linguistic rules incrementally. The results show that this strategy can significantly improve the performance of LLMs, achieving comparable or slightly superior results to humans when translating the unseen languages to English. However, LLMs still struggle with translating English into the unseen languages, typically with complex syntactic rules. We further observe that LLMs cannot deal with languages with object-subject and noun-adjective word order compared to others, reflecting the potential impact imposed by typological features of languages in training data.
Next-Level Cantonese-to-Mandarin Translation: Fine-Tuning and Post-Processing with LLMs (2025)

Large Language Models (LLMs) have improved performance across various natural language processing tasks. Despite these improvements, LLMs continue to face significant challenges, such as grammatical issues and code-switching to English, when applied to low-resource languages like Cantonese in Machine Translation (MT) scenarios. By addressing the unique linguistic and contextual challenges of Cantonese, we present a novel strategy to improve the understanding and translation capabilities of LLMs for Cantonese-to-Mandarin MT. Our strategy comprises three key components: (1) Syntax and Part-of-Speech (POS) fine-tuning, where we use the Universal Dependencies (UD) corpus to fine-tune LLM, focusing on the linguistic structures of Cantonese; (2) Specialized Cantonese to Mandarin sentence pairs, collected from diverse sources such as Cantonese grammar textbooks and manually translated sentences across various domains, to expose the model to a wide range of linguistic contexts; (3) Post-processing with additional LLMs, where we introduce additional LLMs to improve the initial translations, correcting Mandarin grammar and punctuation. Empirical evaluations on human-created test sets show that our proposed strategy improves translation performance and outperforms existing commercial translation models with at least 3 BLEU scores. Additionally, our strategy also benefits other LLMs and a reversed translation direction, demonstrating its generalization and effectiveness.
When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages (2025)

This paper investigates the reference-less evaluation of machine translation for low-resource language pairs, known as quality estimation (QE). Segment-level QE is a challenging cross-lingual language understanding task that provides a quality score (0 -100) to the translated output. We comprehensively evaluate large language models (LLMs) in zero/few-shot scenarios and perform instruction fine-tuning using a novel prompt based on annotation guidelines. Our results indicate that prompt-based approaches are outperformed by the encoder-based fine-tuned QE models. Our error analysis reveals tokenization issues, along with errors due to transliteration and named entities, and argues for refinement in LLM pre-training for cross-lingual tasks. We release the data, and models trained publicly for further research.
Does Machine Translation Impact Offensive Language Identification? The Case of Indo-Aryan Languages (2025)

The accessibility to social media platforms can be improved with the use of machine translation (MT). Non-standard features present in user-generated on social media content such as hashtags, emojis, and alternative spellings can lead to mistranslated instances by the MT systems. In this paper, we investigate the impact of MT on offensive language identification in Indo-Aryan languages. We use both original and MT datasets to evaluate the performance of various offensive language models. Our evaluation indicates that offensive language identification models achieve superior performance on original data than on MT data, and that the models trained on MT data identify offensive language more precisely on MT data than the models trained on original data.
IsiZulu noun classification based on replicating the ensemble approach for Runyankore (2025)

A noun`s class is a crucial component in NLP, because it governs agreement across the sentence in Niger Congo B (NCB) languages, among others. The phenomenon is ill-documented in most NCB languages, or in a non-reusable format, such as a printed dictionary subject to copyright restrictions. A promising approach by Byamugisha (2022) used a data-driven approach for Runyankore that combined syntax and semantics. The code and data are inaccessible however, and it remains to be seen whether it is suitable for other NCB languages. We aimed to reproduce Byamugisha`s experiment, but then for isiZulu. We conducted this as two independent experiments, so that we also could subject it to a meta-analysis. Results showed that it was reproducible only in part, mainly due to imprecision in the original description, and the current impossibility to generate the same kind of source data set generated from an existing grammar. The different choices made in attempting to reproduce the pipeline as well as differences in choice of training and test data had a large effect on the eventual accuracy of noun class disambiguation but could produce accuracies in the same range as for Runyankore: 80-85{\%}.
From Arabic Text to Puzzles: LLM-Driven Development of Arabic Educational Crosswords (2025)

We present an Arabic crossword puzzle generator from a given text that utilizes advanced language models such as GPT-4-Turbo, GPT-3.5-Turbo, and Llama3-8B-Instruct, specifically developed for educational purposes, this innovative generator leverages a meticulously compiled dataset named Arabic-Clue-Instruct with over 50,000 entries encompassing text, answers, clues, and categories. This dataset is intricately designed to aid in the generation of pertinent clues linked to specific texts and keywords within defined categories. This project addresses the scarcity of advanced educational tools tailored for the Arabic language, promoting enhanced language learning and cognitive development. By providing a culturally and linguistically relevant tool, our objective is to make learning more engaging and effective through gamification and interactivity. Integrating state-of-the-art artificial intelligence with contemporary learning methodologies, this tool can generate crossword puzzles from any given educational text, thereby facilitating an interactive and enjoyable learning experience. This tool not only advances educational paradigms but also sets a new standard in interactive and cognitive learning technologies.
Enhance Contextual Learning in ASR for Endangered Low-resource Languages (2025)

Automatic Speech Recognition (ASR) facilitates documenting endangered low-resource languages. While recent advances in acoustic modelling have been substantial, contextual learning remains underexplored. This study investigates the main factors that influence the integration of knowledge from language models (LMs) into state-of-the-art ASR models for endangered low-resource languages. Through experiments on five diverse low-resource languages, we find: 1) Fine-grained tokenization effectively improves ASR performance by addressing the prevalent unknown words and improving data usage efficiency; 2) The integration of transformer-based LMs into ASR systems surpasses that of N-gram LMs only in one language, even though they consistently achieve better results in language modelling tasks. 3) ASR performance is highly sensitive to language-specific optimization, as shown by a 43{\%} performance degradation in one language due to parameter transfer across languages. We open-source our scripts to support further research and applications.
Empowering Low-Resource Languages: TraSe Architecture for Enhanced Retrieval-Augmented Generation in Bangla (2025)

Research on Retrieval-Augmented Generation for low-resource languages has been sparse because of limited resources. To address this, we focus on Bangla, a low-resource language, and have created a dataset of 200 question-answer pairs as a basis for our study from Bangla Wikipedia dumps data. This paper introduces the TraSe architecture, which enhances RAG for Bangla using Translative prompting. Our experiments demonstrate that TraSe improves answer selection accuracy, achieving 34{\%} with automatic retrieval and 63{\%} with Human-in-the-Loop retrieval, outperforming baseline methods. The TraSe architecture marks a significant advancement in RAG for low-resource languages and has the potential to enhance question-answering systems for Bangla and similar languages. Future research could explore additional low-resource languages. The code is available at the following GitHub repository: https://github.com/Atia6/TraSe-Bangla-RAG.
ABDUL: A New Approach to Build Language Models for Dialects Using Formal Language Corpora Only (2025)

Arabic dialects present major challenges for natural language processing (NLP) due to their diglossic nature, phonetic variability, and the scarcity of resources. To address this, we introduce a phoneme-like transcription approach that enables the training of robust language models for North African Dialects (NADs) using only formal language data, without the need for dialect-specific corpora.Our key insight is that Arabic dialects are highly phonetic, with NADs particularly influenced by European languages. This motivated us to develop a novel approach in which we convert Arabic script into a Latin-based representation, allowing our language model, ABDUL, to benefit from existing Latin-script corpora.Our method demonstrates strong performance in multi-label emotion classification and named entity recognition (NER) across various Arabic dialects. ABDUL achieves results comparable to or better than specialized and multilingual models such as DarijaBERT, DziriBERT, and mBERT. Notably, in the NER task, ABDUL outperforms mBERT by 5{\%} in F1-score for Modern Standard Arabic (MSA), Moroccan, and Algerian Arabic, despite using a vocabulary four times smaller than mBERT.
Untangling the Influence of Typology, Data, and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging (2025)

Cross-lingual transfer learning is an invaluable tool for overcoming data scarcity, yet selecting a suitable transfer language remains a challenge. The precise roles of linguistic typology, training data, and model architecture in transfer language choice are not fully understood. We take a holistic approach, examining how both dataset-specific and fine-grained typological features influence transfer language selection for part-of-speech tagging, considering two different sources for morphosyntactic features. While previous work examines these dynamics in the context of bilingual biLSTMS, we extend our analysis to a more modern transfer learning pipeline: zero-shot prediction with pretrained multilingual models. We train a series of transfer language ranking systems and examine how different feature inputs influence ranker performance across architectures. Word overlap, type-token ratio, and genealogical distance emerge as top features across all architectures. Our findings reveal that a combination of typological and dataset-dependent features leads to the best rankings, and that good performance can be obtained with either feature group on its own.
Serving the Underserved: Leveraging BARTBahnar Language Model for Bahnaric-Vietnamese Translation (2025)

The Bahnar people, one of Vietnam`s ethnic minorities, represent an underserved community with limited access to modern technologies. Developing an effective Bahnaric-Vietnamese translation system is essential for fostering linguistic exchange, preserving cultural heritage, and empowering local communities by bridging communication barriers. With advancements in Artificial Intelligence (AI), Neural Machine Translation (NMT) has achieved remarkable success across various language pairs. However, the low-resource nature of Bahnaric, characterized by data scarcity, vocabulary constraints, and the lack of parallel corpora, poses significant challenges to building an accurate and efficient translation system. To address these challenges, we propose a novel hybrid architecture for Bahnaric-Vietnamese translation, with BARTBahnar as its core language model. BARTBahnar is developed by continually training a pre-trained Vietnamese model, BARTPho, on augmented monolingual Bahnaric data, followed by fine-tuning on bilingual datasets. This transfer learning approach reduces training costs while effectively capturing linguistic similarities between the two languages. Additionally, we implement advanced data augmentation techniques to enrich and diversify training data, further enhancing BARTBahnar`s robustness and translation accuracy. Beyond leveraging the language model, our hybrid system integrates rule-based and statistical methods to improve translation quality. Experimental results show substantial improvements on bilingual Bahnaric-Vietnamese datasets, validating the effectiveness of our approach for low-resource translation. To support further research, we open-source our code and related materials at https://github.com/ura-hcmut/BARTBahnar.
Caption Generation in Cultural Heritage: Crowdsourced Data and Tuning Multimodal Large Language Models (2025)

Automated caption generation for paintings enables enhanced access and understanding of visual artworks. This work introduces a novel caption dataset, obtained by manual annotation of about 7500 images from the publicly available DEArt dataset for object detection and pose estimation. Our focus is on describing the visual scenes rather than the context or style of the artwork - more common in other existing captioning datasets. The dataset is the result of a crowdsourcing initiative spanning 13 months, with volunteers adhering to explicit captioning guidelines reflecting our requirements. We provide each artwork in the dataset with five captions, created independently by volunteers to ensure diversity of interpretation and increase the robustness of the captioning model. In addition, we explore using the crowdsourced dataset for fine-tuning Large Language Models with vision encoders for domain-specific caption generation. The goal is to improve the performance of multimodal LLMs in the context of cultural heritage, a domain with {\textquotedblleft}small data{\textquotedblright} which often struggles with the nuanced visual analysis and interpretation required for cultural objects such as paintings. The use of crowdsourced data in the domain adaptation process enables us to incorporate the collective perceptual insights of diverse annotators, resulting in an exploration of visual narratives and observing a reduction in hallucinations otherwise produced by these large language models.
Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems (2025)

Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance, leading to translations that marginalize linguistic diversity. To address these challenges, we propose a multi-agent AI framework designed for culturally adaptive translation in underserved language communities. Our approach leverages specialized agents for translation, interpretation, content synthesis, and bias evaluation, ensuring that linguistic accuracy and cultural relevance are preserved. Using CrewAI and LangChain, our system enhances contextual fidelity while mitigating biases through external validation. Comparative analysis shows that our framework outperforms GPT-4o, producing contextually rich and culturally embedded translations{---}a critical advancement for Indigenous, regional, and low-resource languages. This research underscores the potential of multi-agent AI in fostering equitable, sustainable, and culturally sensitive NLP technologies, aligning with the AI Governance, Cultural NLP, and Sustainable NLP pillars of Language Models for Underserved Communities. Our full experimental codebase is publicly avail able at: github.com/ciol-researchlab/Context-Aware{\_}Translation{\_}MAS.
Enhancing Small Language Models for Cross-Lingual Generalized Zero-Shot Classification with Soft Prompt Tuning (2025)

In NLP, Zero-Shot Classification (ZSC) has become essential for enabling models to classify text into categories unseen during training, particularly in low-resource languages and domains where labeled data is scarce. While pretrained language models (PLMs) have shown promise in ZSC, they often rely on large training datasets or external knowledge, limiting their applicability in multilingual and low-resource scenarios.Recent approaches leveraging natural language prompts reduce the dependence on large training datasets but struggle to effectively incorporate available labeled data from related classification tasks, especially when these datasets originate from different languages or distributions. Moreover, existing prompt-based methods typically rely on manually crafted prompts in a specific language, limiting their adaptability and effectiveness in cross-lingual settings.To address these challenges, we introduce RoSPrompt, a lightweight and data-efficient approach for training soft prompts that enhance cross-lingual ZSC while ensuring robust generalization across data distribution shifts. RoSPrompt is designed for small multilingual PLMs, enabling them to leverage high-resource languages to improve performance in low-resource settings without requiring extensive fine-tuning or high computational costs. We evaluate our approach on multiple multilingual PLMs across datasets covering 106 languages, demonstrating strong cross-lingual transfer performance and robust generalization capabilities over unseen classes.
Cognate and Contact-Induced Transfer Learning for Hamshentsnag: A Low-Resource and Endangered Language (2025)

This study investigates zero-shot and few-shot cross-lingual transfer effects in Part-of-Speech (POS) tagging and Named Entity Recognition (NER) for Hamshentsnag, an endangered Western Armenian dialect. We examine how different source languages, Western Armenian (contact cognate), Eastern Armenian (ancestral cognate), Turkish (substrate or contact-induced), and English (non-cognate), affect the task performance using multilingual BERT and BERTurk. Results show that cognate varieties improved POS tagging by 8{\%} F1, while the substrate source enhanced NER by 15{\%} F1. BERTurk outperformed mBERT on NER but not on POS. We attribute this to task-specific advantages of different source languages. We also used script conversion and phonetic alignment with the target for non-Latin scripts, which alleviated transfer.