Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks

• URL: http://arxiv.org/abs/2504.18838v1
• Date: Sat, 26 Apr 2025 07:48:52 GMT
• Title: Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks
• Authors: Yixin Cao, Shibo Hong, Xinze Li, Jiahao Ying, Yubo Ma, Haiyuan Liang, Yantao Liu, Zijun Yao, Xiaozhi Wang, Dan Huang, Wenxuan Zhang, Lifu Huang, Muhao Chen, Lei Hou, Qianru Sun, Xingjun Ma, Zuxuan Wu, Min-Yen Kan, David Lo, Qi Zhang, Heng Ji, Jing Jiang, Juanzi Li, Aixin Sun, Xuanjing Huang, Tat-Seng Chua, Yu-Gang Jiang,
• Abstract summary: This survey probes the core challenges that the rise of Large Language Models poses for evaluation.<n>We identify and analyze two pivotal transitions: (i) from task-specific to capability-based evaluation, which reorganizes benchmarks around core competencies such as knowledge, reasoning, instruction following, multi-modal understanding, and safety.<n>We will dissect this issue, along with the core challenges of the above two transitions, from the perspectives of methods, datasets, evaluators, and metrics.
• Score: 229.73714829399802
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, this survey probes the core challenges that the rise of LLMs poses for evaluation. We identify and analyze two pivotal transitions: (i) from task-specific to capability-based evaluation, which reorganizes benchmarks around core competencies such as knowledge, reasoning, instruction following, multi-modal understanding, and safety; and (ii) from manual to automated evaluation, encompassing dynamic dataset curation and "LLM-as-a-judge" scoring. Yet, even with these transitions, a crucial obstacle persists: the evaluation generalization issue. Bounded test sets cannot scale alongside models whose abilities grow seemingly without limit. We will dissect this issue, along with the core challenges of the above two transitions, from the perspectives of methods, datasets, evaluators, and metrics. Due to the fast evolving of this field, we will maintain a living GitHub repository (links are in each section) to crowd-source updates and corrections, and warmly invite contributors and collaborators.

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