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This paper empirically evaluates the alignment between LLM-generated and human reviews for scientific papers, finding limited and variable alignment. It also shows that authors can 'game' LLM reviews by iteratively revising papers to improve scores, with up to 35% of papers seeing statistically significant score increases.
This paper investigates the alignment of LLM-generated reviews with human judgment using 1k real ACL 2025 submissions, finding limited agreement, instability across models/prompts, and a method to artificially inflate scores without meaningful changes. The authors advise against relying solely on LLM reviews and call for discussion on their use in handling increasing submission volumes.