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--- |
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license: mit |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- chemistry |
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- physics |
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- mathematics |
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pretty_name: jeebench |
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size_categories: |
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- n<1K |
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--- |
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# JEEBench(EMNLP 2023) |
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Repository for the code and dataset for the paper: "Have LLMs Advanced Enough? A Harder Problem Solving Benchmark For Large Language Models" accepted in EMNLP 2023 as a Main conference paper. |
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https://aclanthology.org/2023.emnlp-main.468/ |
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## Citation |
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If you use our dataset in your research, please cite it using the following |
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```latex |
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@inproceedings{arora-etal-2023-llms, |
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title = "Have {LLM}s Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models", |
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author = "Arora, Daman and |
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Singh, Himanshu and |
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{Mausam}", |
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editor = "Bouamor, Houda and |
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Pino, Juan and |
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Bali, Kalika", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.468", |
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doi = "10.18653/v1/2023.emnlp-main.468", |
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pages = "7527--7543", |
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abstract = "The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs. We curate 515 challenging pre-engineering mathematics, physics and chemistry problems from the highly competitive IIT JEE-Advanced exam. Long-horizon reasoning on top of deep in-domain knowledge is essential for solving problems in this benchmark. Our evaluation on various open-source and proprietary models reveals that the highest performance, even after using techniques like self-consistency, self-refinement and chain-of-thought prompting, is less than 40{\%}. The typical failure modes of GPT-4, the best model, are errors in algebraic manipulation, difficulty in grounding abstract concepts into mathematical equations accurately and failure in retrieving relevant domain-specific concepts. We also observe that by mere prompting, GPT-4 is unable to assess risk introduced by negative marking for incorrect answers. For this, we develop a post-hoc confidence-thresholding method over self-consistency, which enables effective response selection. We hope that our challenging benchmark will guide future re-search in problem-solving using LLMs.", |
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} |
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``` |