Papers by Sheikh Shafayat
BEnQA: A Question Answering Benchmark for Bengali and English (2024.findings-acl)
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| Challenge: | a dataset of parallel Bengali and English exam questions is used to compare LLMs in low-resource languages. |
| Approach: | They introduce BEnQA, a dataset comprising parallel Bengali and English exam questions . they benchmark several Large Language Models with their parallel dataset and observe performance disparity . |
| Outcome: | The proposed dataset consists of 5K questions covering several subjects in science . the authors find that the models perform poorly in Bengali and English . |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |
LangBridge: Multilingual Reasoning Without Multilingual Supervision (2024.acl-long)
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| Challenge: | Existing approaches to adapt language models for multilingual reasoning tasks require multilingual supervision. |
| Approach: | They propose a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision by bridging two models by introducing minimal trainable parameters between them. |
| Outcome: | The proposed approach significantly improves multilingual reasoning capabilities on low-resource languages. |