Summary of L3cube-indicquest: a Benchmark Question Answering Dataset For Evaluating Knowledge Of Llms in Indic Context, by Pritika Rohera et al.
L3Cube-IndicQuest: A Benchmark Question Answering Dataset for Evaluating Knowledge of LLMs in Indic Context
by Pritika Rohera, Chaitrali Ginimav, Akanksha Salunke, Gayatri Sawant, Raviraj Joshi
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the L3Cube-IndicQuest, a benchmark dataset designed to evaluate the regional knowledge of Large Language Models (LLMs) in various Indic languages. The dataset contains 200 question-answer pairs for English and 19 Indic languages, covering five domains specific to the Indic region. This is crucial because current multilingual models are largely evaluated on their performance with globally dominant languages like English, but not necessarily designed to capture regional knowledge in other languages. The authors 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a special language test to see how well computers can understand information about India and its languages. Right now, there’s no standard way to measure this, so the researchers created a test with questions and answers that cover important topics like culture, history, and more. They want other experts to use this test to evaluate how good their computer models are at understanding Indian things. |
