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Local LLM judges show high consistency but low agreement with human ratings

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv evaluates the reliability of local Large Language Models (LLMs) when used as judges for other models. Researchers found that while models like LLaMA-3-8B and Qwen2.5-7B exhibit high self-consistency in their scoring, their agreement with human judgments is limited. LLaMA-3-8B showed a Pearson correlation of 0.275 with human scores, and Qwen2.5-7B achieved 0.340, indicating a significant gap between internal consistency and external validity. AI

IMPACT Highlights the need for careful evaluation of LLM judges to ensure alignment with human judgment, impacting how AI models are assessed.

RANK_REASON Research paper evaluating LLM judges. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Local LLM judges show high consistency but low agreement with human ratings

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Research paper evaluating LLM judges. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Aakash Kumar Tiwari ·

    When Consistency Does Not Mean Reliability: Evaluating Local LLM Judges Against Human Ratings

    arXiv:2609.13824v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to evaluate the responses of other language models. This approach, known as LLM-as-a-Judge, is faster and cheaper than human evaluation. However, a judge may produce consistent scor…

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