Models Know Models Best: Evaluation via Model-Preferred Formats

• URL: http://arxiv.org/abs/2601.22699v1
• Date: Fri, 30 Jan 2026 08:15:41 GMT
• Title: Models Know Models Best: Evaluation via Model-Preferred Formats
• Authors: Joonhak Lee, Sungmok Jung, Jongyeon Park, Jaejin Lee,
• Abstract summary: Performance of Large Language Models on multiple-choice tasks differs markedly between symbol-based and cloze-style evaluation formats.<n>A dynamic format-alignment strategy is introduced that employs a lightweight classifier trained on latent model-preference signals.<n>The proposed method achieves substantial and consistent improvements in zero-shot accuracy across reasoning and knowledge benchmarks.
• Score: 2.9923891863939946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Performance of Large Language Models (LLMs) on multiple-choice tasks differs markedly between symbol-based and cloze-style evaluation formats. The observed discrepancies are systematically attributable to task characteristics: natural language continuation benefits from likelihood scoring, whereas explicit comparison is better suited to symbol-based selection. These trends are consistent across various decoder-based LLMs, indicating model-agnostic effects. To address these inconsistencies, a dynamic format-alignment strategy is introduced that employs a lightweight classifier trained on latent model-preference signals. In contrast to human-designed heuristics, which often degrade performance, this approach uses model-generated signals to determine the optimal format for each problem instance. The proposed method achieves substantial and consistent improvements in zero-shot accuracy across reasoning and knowledge benchmarks, better revealing the models' latent capabilities.

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