Papers by Rifki Putri
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 . |
Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being used to generate synthetic data for training and evaluating models. |
| Approach: | They investigate the effectiveness of using Large Language Models to generate culturally relevant commonsense QA datasets for Indonesian and Sundanese languages using both LLMs and human annotators. |
| Outcome: | The proposed model generates 4.5K questions per language, compared with 4.5k for Indonesian and 4.5km for Sundanese. |