"Decode the Law": Towards Legal Text Simplification with Large Language Models

QuALA-NL: Question & Answer with Legal Attribution in Dutch

Multilingual in Name Only? Cultural and Linguistic Weaknesses of LLMs in Urdu

The notable findings suggest that LLMs often make basic errors of grammar and semantics, and highlight the limitations of current LLMs as a reliable source of content generation and information retrieval for low-resource languages.

Breaking Bureaucracy: Evaluating open-source LLMs for legal document review

In this paper, we evaluate open-source generative LLMs on legal Natural Language Inference (NLI). Legal inspectorial processes take place in specific domains and often deal with confidential data. This creates a need for working with local models that do not require labeled training data. We evaluate our models on the...

AssamLegalTrans: A Parallel Corpus, Benchmark and Analysis for English-Assamese Machine Translation of Legal Judgments

This work introduces the first-of-its-kind English-Assamese parallel corpus for the translation of Indian court judgments and performs a comprehensive evaluation of state-of-the-art multilingual models in both zero-shot and fine-tuned settings, comparing their performance against commercial systems.

Do Small Models Use the Law You Give Them? Measuring Context Use on a Bilingual Bangladesh Legal Benchmark

Fine-tuning can improve legal question-answering accuracy without improving how models use law supplied in context. We study this distinction in bilingual Bangladeshi legal QA, where observed errors can arise from answer scoring, retrieval, or failure to use relevant law. We construct a hierarchy-preserving statutory c...

Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks

This work introduces an alternative inspired by language-learning assessment, using an open-weight-LLM QA protocol that measures salient content preservation that aligns more closely with human rankings and is six to seven times more paraphrase-invariant than BLEU-4.