Prompting strategies in LLM evaluations are judge-dependent, study finds

A new study published on arXiv investigates the effectiveness of prompting strategies like Chain-of-Thought and Role Conditioning in multilingual LLM-as-a-Judge evaluations. The research found that the choice of judge model significantly impacts these strategies, with effects often reversing direction across different judges and degrading in low-resource languages. The findings suggest that prompting strategies must be validated for specific evaluation settings, and cross-study comparisons should account for both judge and prompt configurations. AI

IMPACT Highlights the need for careful validation of LLM evaluation methods across different languages and models to ensure reliable results.

RANK_REASON Academic paper on LLM evaluation methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Hugging Face
• LLM-as-a-Judge
• Role Conditioning

• paper
• other

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