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About this paper
Xue Liu, Xin Ma, Yuxin Ma, Yongchang Peng, Duo Wang, Zhoufutu Wen, Ge Zhang, Kaiyuan Zhang and 31 more
The XpertBench benchmark evaluates Large Language Models on complex, open-ended expert-level tasks across seven professional domains, using granular, expert-curated rubrics. Evaluations on state-of-the-art models revealed an "expert-gap" with top models achieving around 66% success and showed pronounced domain-specific specialization rather than universal expert capability.
Key insights
- State-of-the-art LLMs exhibit a notable "expert-gap," with top models achieving approximately 66% success on complex professional tasks, highlighting limitations in robust reasoning and knowledge synthesis.
- LLM "expert intelligence" is not uniform; models show distinct domain-specific specializations, indicating that a single, universally capable expert AI has not yet emerged.
- Observed failure modes include "retrieval interference" from extraneous web data and "principle hallucinations" where early conceptual errors cascade into incoherent reasoning.
Summary by alphaXiv.
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