ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2411.01533
79
1
v1v2v3 (latest)

Enhancing LLM Evaluations: The Garbling Trick

3 November 2024
William F. Bradley
    LRMELM
ArXiv (abs)PDFHTML
Main:9 Pages
4 Figures
Bibliography:3 Pages
Appendix:3 Pages
Abstract

As large language models (LLMs) become increasingly powerful, traditional evaluation metrics tend to saturate, making it challenging to distinguish between models based on their performance. We propose a general method to transform existing LLM evaluations into a series of progressively more difficult tasks. These enhanced evaluations emphasize reasoning capabilities and can reveal relative performance differences that are not apparent in the original assessments. To demonstrate the effectiveness of our approach, we create a new multiple-choice test corpus, extend it into a family of evaluations, and assess a collection of LLMs. Our results offer insights into the comparative reasoning abilities of these models, particularly highlighting distinctions between OpenAI's o1-preview and Google's gemini-pro-1.5-002.

View on arXiv
@article{bradley2025_2411.01533,
  title={ Enhancing LLM Evaluations: The Garbling Trick },
  author={ William F. Bradley },
  journal={arXiv preprint arXiv:2411.01533},
  year={ 2025 }
}
Comments on this paper