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LLMs Show Moderate Correlation with Human Judgment in Argument Quality Assessment

By PulseAugur Editorial · [2 sources] ·

Researchers have explored the use of Large Language Models (LLMs) for assessing argument quality, comparing 12 open-weight models. The study found that LLMs show promising, though moderate, correlation with human expert judgments. Llama-70B demonstrated the strongest alignment with experts, achieving a moderate Cohen's \u03ba = 0.493. The findings suggest that LLMs can partially but complementarily understand argument quality dimensions, with their predictions remaining stable across multiple runs. AI

IMPACT LLMs demonstrate a moderate ability to assess argument quality, with Llama-70B showing the strongest alignment with human experts.

RANK_REASON The cluster contains a research paper detailing a novel approach using LLMs for argument quality assessment.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs Show Moderate Correlation with Human Judgment in Argument Quality Assessment

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The cluster contains a research paper detailing a novel approach using LLMs for argument quality assessment.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Nicol\'as Benjam\'in Ocampo, Agnes Paullate Nyiranziza, Davide Ceolin ·

    Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach

    arXiv:2605.28313v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in tasks related to reasoning and judgment. However, assessing the quality of arguments requires a rigorous evaluation. We investigate the extent to which LLMs c…

  2. arXiv cs.CL TIER_1 English(EN) · Davide Ceolin ·

    Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach

    Large Language Models (LLMs) have demonstrated remarkable capabilities in tasks related to reasoning and judgment. However, assessing the quality of arguments requires a rigorous evaluation. We investigate the extent to which LLMs can effectively perform this task. We tested 12 o…

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