Papers by Ishaan Watts
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks (2024.naacl-long)
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Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, Ishaan Watts, Ashutosh Sathe, Millicent Ochieng, Rishav Hada, Prachi Jain, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Several new LLMs have been introduced necessitating their evaluation on non-English languages. |
| Approach: | They perform a thorough evaluation of the non-English capabilities of SoTA LLMs by comparing them on the same set of multilingual datasets. |
| Outcome: | The proposed model outperforms models on multilingual datasets on 22 languages including low-resource African languages. |
MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models (2024.findings-acl)
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| Challenge: | Prior work on multilingual evaluation has shown that there is a large gap between the performance of Large Language Models on English and other languages. |
| Approach: | They propose to finetune Llama-2 and Mistral models on two datasets to determine their effect on model performance on six downstream tasks covering forty one languages. |
| Outcome: | The proposed model can improve on six multilingual tasks while degrading on high-resource languages. |
PARIKSHA: A Large-Scale Investigation of Human-LLM Evaluator Agreement on Multilingual and Multi-Cultural Data (2024.emnlp-main)
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| Challenge: | Evaluation of multilingual Large Language Models is challenging due to a variety of factors including the lack of benchmarks with sufficient linguistic diversity, contamination of popular benchmarks into LLM pre-training data and lack of local, cultural nuances in translated benchmarks. |
| Approach: | They evaluate 30 models across 10 Indic languages by conducting 90K human evaluations and 30K LLM-based evaluations. |
| Outcome: | The proposed models perform best in most Indic languages, while the agreement drops for direct assessment especially for Bengali and Odia. |