LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models

• URL: http://arxiv.org/abs/2511.11334v1
• Date: Fri, 14 Nov 2025 14:13:07 GMT
• Title: LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models
• Abstract summary: We introduce LaoBench, the first large-scale, high-quality, and multidimensional benchmark dataset dedicated to assessing large language models (LLMs)<n>LaoBench comprises over 17,000 carefully curated samples spanning three core dimensions: knowledge application, K12 foundational education, and bilingual translation among Lao, Chinese, and English.<n>Our data construction pipeline integrates expert human curation with automated agent-assisted verification, ensuring linguistic accuracy, cultural relevance, and educational value.
• Score: 13.28738007425811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of large language models (LLMs) has not been matched by their evaluation in low-resource languages, especially Southeast Asian languages like Lao. To fill this gap, we introduce LaoBench, the first large-scale, high-quality, and multidimensional benchmark dataset dedicated to assessing LLMs' comprehensive language understanding and reasoning abilities in Lao. LaoBench comprises over 17,000 carefully curated samples spanning three core dimensions: knowledge application, K12 foundational education, and bilingual translation among Lao, Chinese, and English. The dataset is divided into open-source and closed-source subsets, with the closed-source portion enabling black-box evaluation on an official platform to ensure fairness and data security. Our data construction pipeline integrates expert human curation with automated agent-assisted verification, ensuring linguistic accuracy, cultural relevance, and educational value. Benchmarking multiple state-of-the-art LLMs on LaoBench reveals that current models still face significant challenges in mastering Lao across diverse tasks. We hope LaoBench will catalyze further research and development of AI technologies for underrepresented Southeast Asian languages.

Related papers

• MameLoshnLM: Yiddish Language Model and Evaluation Benchmark [62.936233688546984]
  We present MameLoshnLM, the first open-source 8B- parameter language model built specifically for Yiddish.<n>Despite Yiddish's rich textual tradition, its limited digital presence and the scarcity of reliable evaluation resources have constrained progress in Yiddish language modeling.<n>Our results provide both a foundation for Yiddish NLP and a practical template for language model development in historically rich but digitally underrepresented languages.
  arXiv  Detail & Related papers  (2026-08-06T10:24:08Z)
• BYOL: Bring Your Own Language Into LLMs [12.151176703151428]
  Large Language Models (LLMs) exhibit strong multilingual capabilities, yet remain constrained by the severe imbalance in global language resources.<n>This disparity leads to systematic underperformance, cultural misalignment, and limited accessibility for speakers of low-resource and extreme-low-resource languages.<n>We introduce Bring Your Own Language (BYOL), a unified framework for scalable, language-aware LLM development tailored to each language's digital footprint.
  arXiv  Detail & Related papers  (2026-01-15T19:15:13Z)
• Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus [3.435561406656216]
  The linguistic diversity of India poses significant machine translation challenges, especially for underrepresented tribal languages like Bhili.<n>This paper introduces Bhili-Hindi-English Parallel Corpus (BH EPC), the first and largest parallel corpus worldwide comprising 110,000 meticulously curated sentences across Bhili, Hindi, and English.<n>BH EPC spans critical domains such as education, administration, and news, establishing a valuable benchmark for research in low resource machine translation.
  arXiv  Detail & Related papers  (2025-11-01T10:39:56Z)
• The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks [0.0]
  We introduce the AI Language Monitor, a comprehensive benchmark that assesses large language models (LLMs) performance across up to 200 languages.<n>Our benchmark aggregates diverse tasks including translation, question answering, math, and reasoning, using datasets such as FLORES+, MMLU, GSM8K, TruthfulQA, and ARC.<n>We provide an open-source, auto-updating leaderboard and dashboard that supports researchers, developers, and policymakers in identifying strengths and gaps in model performance.
  arXiv  Detail & Related papers  (2025-07-11T12:38:02Z)
• Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering [73.73820209993515]
  We introduce KoLasSimpleQA, the first benchmark evaluating the multilingual factual ability of Large Language Models (LLMs)<n>Inspired by existing research, we created the question set with features such as single knowledge point coverage, absolute objectivity, unique answers, and temporal stability.<n>Results show significant performance differences between the two domains.
  arXiv  Detail & Related papers  (2025-05-22T12:27:02Z)
• IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models [18.083861654053585]
  This paper introduces IrokoBench -- a human-translated benchmark dataset for 17 typologically-diverse low-resource African languages.<n>We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings(where test sets are translated into English) across 10 open and six proprietary language models.<n>We observe a significant performance gap between open and proprietary models, with the highest performing open model, Gemma 2 27B only at 63% of the best-performing proprietary model GPT-4o performance.
  arXiv  Detail & Related papers  (2024-06-05T15:23:08Z)
• High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models [5.632410663467911]
  We explore the extent to which pretrained large language models (LLMs) can bridge the performance gap for under-resourced languages. We find that LLMs easily set the state of the art for the under-resourced languages by substantial margins. For all our languages, human evaluation shows on-a-par performance with humans for our best systems, but BLEU scores collapse compared to English.
  arXiv  Detail & Related papers  (2024-02-19T16:29:40Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• YAYI 2: Multilingual Open-Source Large Language Models [53.92832054643197]
  We propose YAYI 2, including both base and chat models, with 30 billion parameters. YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback.
  arXiv  Detail & Related papers  (2023-12-22T17:34:47Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages [86.90220551111096]
  Training datasets for large language models (LLMs) are often not fully disclosed. We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages.
  arXiv  Detail & Related papers  (2023-09-17T23:49:10Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.