The paper investigates whether fine‑tuning improves how language models use supplied Bangladeshi legal text in bilingual question‑answering. Using a hierarchy‑preserving statutory corpus, 2,165 fine‑tuning examples, and a 150‑item control set, the authors evaluate six instruction‑tuned models with multiple LoRA seeds, separating scoring, retrieval, and model effects. Results show that while fine‑tuning can boost overall accuracy, it does not increase the models’ reliance on the governing provision, highlighting the need to disentangle scorer, retriever, and model contributions in legal adaptation studies.
By Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa, Mir Mohammad Asif Abdullah, Swakkhar Shatabda, Md Adnan Arefeen
The paper presents a smartphone‑compatible, retrieval‑augmented language model tailored to Bangladeshi statutory law. By distilling a 9‑billion‑parameter Gemma‑2 teacher into a 2‑billion‑parameter student using supervised fine‑tuning and QLoRA, the authors achieve significant gains in ROUGE‑L and BERTScore on an English benchmark while keeping the model lightweight (1.6 GB) and operable offline on a Pixel 6. The system retrieves from 36,029 statutory passages using a hybrid dense/BM25 approach, and cross‑lingual evaluation shows effective Bangla query handling against an English‑only corpus, with a practicing lawyer rating the responses highly in a pilot study.
By MD. Nafis Kamal, Mahadi Hasan Fahim, Talha Ridwan, Nadifa Zaman, Fariha Roushon Florin, Farig Yousuf Sadeque, Saadat Rafid Ahmed
arXiv:2610.24764v2 Announce Type: replace
Abstract: This paper presents BanglaBBQ, a question-answering bias benchmark designed to capture stereotypes grounded in the Bangladeshi context, in both Ban...
By Sumit Anthony Gomes, Yasin Rahman, Taeeba Tasnia, Faiza Nooren Suhita
arXiv:2507.23248v2 Announce Type: replace-cross
Abstract: Bengali is spoken by more than 230 million people, yet no standardized instrument evaluates large language models (LLMs) on Bengali across th...
By Shimanto Bhowmik, Tawsif Tashwar Dipto, Md Sazzad Islam, Sheryl Hsu, Tahsin Reasat
arXiv:2606. 18699v1 Announce Type: cross Abstract: Large language models (LLMs) have shown impressive capabilities across diverse tasks, yet their performance on jurisdiction-specific legal reasoning remains underexplored.
By Fei-Yueh Chen, Chun Huang Lin, Chan Wei Hsu, Kuan Hsuan Yeh, Zih-Ching Chen, Kuan-Ming Chen, Patrick Chung-Chia Huang
arXiv:2608.28645v1 Announce Type: cross
Abstract: Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, ther...
By Sindhu Shetty, Spurthi Setty, Natan Vidra
arXiv:2610.32144v1 Announce Type: new
Abstract: When a language model is asked whether its own answer is correct, three languages are involved: that of the question, that requested for the answer, an...
By Jewon Yeom, Jaewon Sok, Seonghyeon Park, Jeongjae Park, Hwiyeong Lee, Taesup Kim
KhatianDoc is a new benchmark that tests multimodal large language models on Bengali legal land records, specifically the handwritten RS Khatians used in Bangladesh. The benchmark comprises four tasks—symbol recognition, base‑16 to decimal conversion, structured field extraction, and legal document question answering—drawn from 107 real records and 1,634 QA pairs. Six multimodal LLMs were evaluated under a zero‑shot protocol, revealing that many models fail to answer a significant portion of questions correctly and perform poorly on arithmetic conversion, highlighting a lack of capability rather than a performance gap.
By Tasmiad Hasan, Arafat Zaman Ratul, Sarker Sadman Saalim, S. M. Shah Nawaz Hossain, Khan Raiyan Ibne Reza, Sumaiya Tabassum Nimi
The paper investigates whether lower stereotype bias scores in multilingual language models truly indicate greater fairness. By auditing ten open models across eight languages, the authors show that lower average scores often arise from translation artifacts rather than genuine bias reduction, as item‑level correlations between English and translated scores are weak. They also identify pipeline errors that can mask bias signals and provide code to replicate their findings.
By Harsh Kumar
arXiv:2609.18284v1 Announce Type: new
Abstract: In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian...
By M\'aty\'as Osv\'ath, Enik\H{o} H\'eja, No\'emi Ligeti-Nagy
arXiv:2605. 25420v2 Announce Type: replace-cross Abstract: Large language model safety evaluation remains heavily English-centered, leaving low-resource languages under-measured even when models are deployed globally.
By Khalid Yusuf Dahir
The paper presents a cross‑lingual legal QA system for Vietnamese labour law, introducing a bilingual evaluation suite of 231 Vietnamese–English question–answer pairs, 75 of which are annotated for five complex legal reasoning phenomena. It evaluates a verifier‑guided pipeline that decomposes answers into claims, checks citation reachability and entailment, and corrects citation failures and contradictions, and introduces six automatic diagnostics for faithfulness to retrieved evidence. Experiments show that dense retrieval outperforms sparse and hybrid retrieval, translation placement has no significant effect on diagnostics, and verifier‑guided correction modestly improves citation preservation but not other dimensions, with human evaluation indicating a gap between automatic diagnostics and human judgments.
By Nguyen Minh Chi, Mo El-Haj, Nguyen Ha Thanh, Dawn Knight, Paul Rayson