L3Cube-IndicQuest: A Benchmark Question Answering Dataset for Evaluating Knowledge of LLMs in Indic Context

• URL: http://arxiv.org/abs/2409.08706v2
• Date: Wed, 30 Oct 2024 10:30:57 GMT
• Title: L3Cube-IndicQuest: A Benchmark Question Answering Dataset for Evaluating Knowledge of LLMs in Indic Context
• Authors: Pritika Rohera, Chaitrali Ginimav, Akanksha Salunke, Gayatri Sawant, Raviraj Joshi,
• Abstract summary: We present the L3Cube-IndicQuest, a gold-standard factual question-answering benchmark dataset. The dataset contains 200 question-answer pairs, each for English and 19 Indic languages, covering five domains specific to the Indic region.
• Score: 0.4194295877935868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have made significant progress in incorporating Indic languages within multilingual models. However, it is crucial to quantitatively assess whether these languages perform comparably to globally dominant ones, such as English. Currently, there is a lack of benchmark datasets specifically designed to evaluate the regional knowledge of LLMs in various Indic languages. In this paper, we present the L3Cube-IndicQuest, a gold-standard factual question-answering benchmark dataset designed to evaluate how well multilingual LLMs capture regional knowledge across various Indic languages. The dataset contains 200 question-answer pairs, each for English and 19 Indic languages, covering five domains specific to the Indic region. We aim for this dataset to serve as a benchmark, providing ground truth for evaluating the performance of LLMs in understanding and representing knowledge relevant to the Indian context. The IndicQuest can be used for both reference-based evaluation and LLM-as-a-judge evaluation. The dataset is shared publicly at https://github.com/l3cube-pune/indic-nlp .

Related papers

• Truth Knows No Language: Evaluating Truthfulness Beyond English [11.20320645651082]
  We introduce a professionally translated extension of the TruthfulQA benchmark designed to evaluate truthfulness in Basque, Catalan, Galician, and Spanish. Our study evaluates 12 state-of-the-art open LLMs, comparing base and instruction-tuned models using human evaluation, multiple-choice metrics, and LLM-as-a-Judge scoring.
  arXiv  Detail & Related papers  (2025-02-13T15:04:53Z)
• Analysis of Indic Language Capabilities in LLMs [0.3599866690398789]
  This report evaluates the performance of text-in text-out Large Language Models (LLMs) to understand and generate Indic languages. Hindi is the most widely represented language in models. While model performance roughly correlates with number of speakers for the top five languages, the assessment after that varies.
  arXiv  Detail & Related papers  (2025-01-23T18:49:33Z)
• ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding [15.93642619347214]
  We introduce proverbeval, LLM evaluation benchmark for low-resource languages. Native language proverb descriptions significantly improve tasks such as proverb generation. monolingual evaluations consistently outperformed their cross-lingual counterparts in generation tasks.
  arXiv  Detail & Related papers  (2024-11-07T06:34:48Z)
• MILU: A Multi-task Indic Language Understanding Benchmark [7.652738829153342]
  We introduce MILU, a comprehensive evaluation benchmark designed to assess Large Language Models in Indic languages. With an India-centric design, MILU incorporates material from regional and state-level examinations, covering topics such as local history, arts, festivals, and laws, alongside standard subjects like science and mathematics. Open multilingual models outperform language-specific fine-tuned models, which perform only slightly better than random baselines.
  arXiv  Detail & Related papers  (2024-11-04T19:17:17Z)
• One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks [68.33068005789116]
  We present the first study aimed at objectively assessing the fairness and robustness of Large Language Models (LLMs) in handling dialects in canonical reasoning tasks. We hire AAVE speakers, including experts with computer science backgrounds, to rewrite seven popular benchmarks, such as HumanEval and GSM8K. Our findings reveal that textbfalmost all of these widely used models show significant brittleness and unfairness to queries in AAVE.
  arXiv  Detail & Related papers  (2024-10-14T18:44:23Z)
• INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages [26.13077589552484]
  Indic-QA is the largest publicly available context-grounded question-answering dataset for 11 major Indian languages from two language families. We generate a synthetic dataset using the Gemini model to create question-answer pairs given a passage, which is then manually verified for quality assurance. We evaluate various multilingual Large Language Models and their instruction-fine-tuned variants on the benchmark and observe that their performance is subpar, particularly for low-resource languages.
  arXiv  Detail & Related papers  (2024-07-18T13:57:16Z)
• Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages [48.40607157158246]
  Large Language Models (LLMs) perform better on high-resource languages like English, German, and French, while their capabilities in low-resource languages remain inadequate. We propose the Language Ranker, an intrinsic metric designed to benchmark and rank languages based on LLM performance using internal representations. Our analysis reveals that high-resource languages exhibit higher similarity scores with English, demonstrating superior performance, while low-resource languages show lower similarity scores.
  arXiv  Detail & Related papers  (2024-04-17T16:53:16Z)
• OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models [59.54423478596468]
  We introduce OMGEval, the first Open-source Multilingual Generative test set that can assess the capability of LLMs in different languages. For each language, OMGEval provides 804 open-ended questions, covering a wide range of important capabilities of LLMs. Specifically, the current version of OMGEval includes 5 languages (i.e., Zh, Ru, Fr, Es, Ar)
  arXiv  Detail & Related papers  (2024-02-21T04:42:41Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization [98.61159823343036]
  We present the Word-in-Context dataset (WiC) for assessing the ability to correctly model distinct meanings of a word. We put forward a large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages. Experimental results show that even when no tagged instances are available for a target language, models trained solely on the English data can attain competitive performance.
  arXiv  Detail & Related papers  (2020-10-13T15:32:00Z)

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.