Update README.md

README.md

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 pretty_name: jeebench
 size_categories:
 - n<1K
----
+---
+
+# JEEBench(EMNLP 2023)
+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. 
+https://aclanthology.org/2023.emnlp-main.468/
+
+
+## Citation
+
+If you use our dataset in your research, please cite it using the following
+```latex
+@inproceedings{arora-etal-2023-llms,
+    title = "Have {LLM}s Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models",
+    author = "Arora, Daman  and
+      Singh, Himanshu  and
+      {Mausam}",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.468",
+    doi = "10.18653/v1/2023.emnlp-main.468",
+    pages = "7527--7543",
+    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.",
+}
+```