@inproceedings{kazemi-etal-2025-big,
title = "{BIG}-Bench Extra Hard",
author = "Kazemi, Mehran and
Fatemi, Bahare and
Bansal, Hritik and
Palowitch, John and
Anastasiou, Chrysovalantis and
Mehta, Sanket Vaibhav and
Jain, Lalit K and
Aglietti, Virginia and
Jindal, Disha and
Chen, Peter and
Dikkala, Nishanth and
Tyen, Gladys and
Liu, Xin and
Shalit, Uri and
Chiappa, Silvia and
Olszewska, Kate and
Tay, Yi and
Tran, Vinh Q. and
Le, Quoc V and
Firat, Orhan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1285/",
doi = "10.18653/v1/2025.acl-long.1285",
pages = "26473--26501",
ISBN = "979-8-89176-251-0",
abstract = "Current benchmarks for large language model (LLM) reasoning predominantly focus on mathematical and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. One particular exception is the BIG-Bench dataset, which has served as a crucial benchmark for evaluating the general reasoning capabilities of LLMs, thanks to its diverse set of challenging tasks that allowed for a comprehensive assessment of general reasoning across various skills within a unified framework. However, recent advances in LLMs have led to saturation on BIG-Bench, and its harder version BIG-Bench Hard (BBH). State-of-the-art models achieve near-perfect scores on many tasks in BBH, thus diminishing its utility. To address this limitation, we introduce BIG-Bench Extra Hard (BBEH), a new benchmark designed to push the boundaries of LLM reasoning evaluation. BBEH replaces each task in BBH with a novel task that probes a similar reasoning capability but exhibits significantly increased difficulty. We evaluate various general-purpose and reasoning-specialized models on BBEH and observe an accuracy of 23.9{\%} for the best general-purpose model and 54.2{\%} for the best reasoning-specialized model, indicating substantial room for improvement and highlighting the ongoing challenge of achieving robust general reasoning in LLMs. We release BBEH publicly at: https://github.com/google-deepmind/bbeh."
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<abstract>Current benchmarks for large language model (LLM) reasoning predominantly focus on mathematical and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. One particular exception is the BIG-Bench dataset, which has served as a crucial benchmark for evaluating the general reasoning capabilities of LLMs, thanks to its diverse set of challenging tasks that allowed for a comprehensive assessment of general reasoning across various skills within a unified framework. However, recent advances in LLMs have led to saturation on BIG-Bench, and its harder version BIG-Bench Hard (BBH). State-of-the-art models achieve near-perfect scores on many tasks in BBH, thus diminishing its utility. To address this limitation, we introduce BIG-Bench Extra Hard (BBEH), a new benchmark designed to push the boundaries of LLM reasoning evaluation. BBEH replaces each task in BBH with a novel task that probes a similar reasoning capability but exhibits significantly increased difficulty. We evaluate various general-purpose and reasoning-specialized models on BBEH and observe an accuracy of 23.9% for the best general-purpose model and 54.2% for the best reasoning-specialized model, indicating substantial room for improvement and highlighting the ongoing challenge of achieving robust general reasoning in LLMs. We release BBEH publicly at: https://github.com/google-deepmind/bbeh.</abstract>
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%0 Conference Proceedings
%T BIG-Bench Extra Hard
%A Kazemi, Mehran
%A Fatemi, Bahare
%A Bansal, Hritik
%A Palowitch, John
%A Anastasiou, Chrysovalantis
%A Mehta, Sanket Vaibhav
%A Jain, Lalit K.
%A Aglietti, Virginia
%A Jindal, Disha
%A Chen, Peter
%A Dikkala, Nishanth
%A Tyen, Gladys
%A Liu, Xin
%A Shalit, Uri
%A Chiappa, Silvia
%A Olszewska, Kate
%A Tay, Yi
%A Tran, Vinh Q.
%A Le, Quoc V.
%A Firat, Orhan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F kazemi-etal-2025-big
%X Current benchmarks for large language model (LLM) reasoning predominantly focus on mathematical and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. One particular exception is the BIG-Bench dataset, which has served as a crucial benchmark for evaluating the general reasoning capabilities of LLMs, thanks to its diverse set of challenging tasks that allowed for a comprehensive assessment of general reasoning across various skills within a unified framework. However, recent advances in LLMs have led to saturation on BIG-Bench, and its harder version BIG-Bench Hard (BBH). State-of-the-art models achieve near-perfect scores on many tasks in BBH, thus diminishing its utility. To address this limitation, we introduce BIG-Bench Extra Hard (BBEH), a new benchmark designed to push the boundaries of LLM reasoning evaluation. BBEH replaces each task in BBH with a novel task that probes a similar reasoning capability but exhibits significantly increased difficulty. We evaluate various general-purpose and reasoning-specialized models on BBEH and observe an accuracy of 23.9% for the best general-purpose model and 54.2% for the best reasoning-specialized model, indicating substantial room for improvement and highlighting the ongoing challenge of achieving robust general reasoning in LLMs. We release BBEH publicly at: https://github.com/google-deepmind/bbeh.
%R 10.18653/v1/2025.acl-long.1285
%U https://aclanthology.org/2025.acl-long.1285/
%U https://doi.org/10.18653/v1/2025.acl-long.1285
%P 26473-26501
Markdown (Informal)
[BIG-Bench Extra Hard](https://aclanthology.org/2025.acl-long.1285/) (Kazemi et al., ACL 2025)
ACL
- Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, and Orhan Firat. 2025. BIG-Bench Extra Hard. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26473–26501, Vienna, Austria. Association for Computational Linguistics.