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[NAACL'25] Dataset and Evaluation Code for LLMs as Meta-Reviewers’ Assistants: A Case Study

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LLM-as-Meta-Reviewer Assistants'

Folder Organization

  • Annotation Files
    • Contains human annotation for micro and macro evaluation.
  • LLM Responses
    • This folder contains responses generated by three LLMs (i.e., PalM2, GPT-3.5, and Llama2) for four prompt levels. Here, LLM1, LLM2, and LLM3 represents PalM2, GPT-3.5, and Llama2. The responses are in the columns res_prompt1,res_prompt2, res_prompt3, and res_prompt4.
  • GPT Ratings
    • Contains the ratings given by GPT-4o on the LLM generated responses. The human and GPT ratings are merged together in the files. GPT_rating_LLMx and Macro_GPT_score contains micro and macro evaluation ratings of both human and GPT-4o respectively.
    • corr_average_likert_ceil file contains the correlations between human and GPT-4o in micro-evaluation for each LLMs.
  • Meta-Review Data
    • The curated dataset used for the meta-review generation. Review1, Review2, and Review3 columns have the peer reviews and Meta_Review column contain the meta-reviews written by humans.

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[NAACL'25] Dataset and Evaluation Code for LLMs as Meta-Reviewers’ Assistants: A Case Study

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