Label-free strategies fail to improve LLM accuracy on multiple-choice benchmarks

A new research paper explores the effectiveness of label-free strategies in evaluating large language models (LLMs) using multiple-choice questions. The study found that common methods, such as generation-then-matching or scoring options in isolation, do not reliably improve accuracy or remove positional bias related to option order. The research indicates that the primary bottleneck is withholding options from the model, and that presenting all options together with an LLM matcher is the most effective configuration, though it doesn't consistently yield accuracy gains. AI

IMPACT This research highlights potential flaws in current LLM evaluation benchmarks, suggesting a need for more robust methods to accurately assess model knowledge.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM evaluation methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• large language models

• paper
• model release

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