Do LLM Debates Repeat Arguments Differently Across Languages?

2026-07-26 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A study investigated argument repetition in LLM debates across languages, introducing "prior-argument similarity" as an aggregate diagnostic. Analyzing 71 motions, six languages, and four model agents in controlled eight-turn debates, the research found Chinese consistently exhibits a positive gap in argument similarity relative to English. This finding proved robust across various controls, including agents, turn positions, regression adjustment, and metric variants. Manual calibration indicated a high-similarity tail enriched for substantive repetition. While a diversity-aware prompt reduced overall prior-argument similarity across languages, it did not significantly narrow the observed Chinese-English gap. The findings suggest multilingual debate evaluation should measure argumentative development over time.

Key takeaway

For NLP Engineers developing or evaluating multilingual LLM debate systems, recognize that argument repetition varies significantly across languages. Chinese debates consistently show higher prior-argument similarity than English, even when using diversity-aware prompts. Therefore, your evaluation metrics must account for argumentative development over time and report language-specific gaps, not just average mitigation effects, to accurately assess system performance and identify cultural or linguistic biases.

Key insights

LLM debate argument repetition varies significantly by language, with Chinese showing higher prior-argument similarity.

Principles

• Multilingual debate evaluation must measure argumentative development over time.
• Report mitigation effects in both average and language-specific gap terms.

Method

A "prior-argument similarity" diagnostic compares extracted argument units with earlier units in the same debate to quantify repetition.

In practice

• Implement prior-argument similarity to assess debate content novelty.
• Use diversity-aware prompts to reduce argument repetition in LLMs.

Topics

• LLM Debates
• Multilingual NLP
• Argument Similarity
• Debate Evaluation
• Chinese Language Models
• Argument Mining

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.