(Towards) Scalable Reliable Automated Evaluation with Large Language Models
Bertil Braun, Martin Forell
Why It Matters
What makes this one worth your time
This framework could streamline the evaluation process of LLMs, making it more scalable and less reliant on human experts, which is crucial for rapidly advancing AI applications.
A new framework uses pairwise comparisons and Elo ratings to evaluate LLM outputs with reduced human input.
Summary
The paper proposes a novel evaluation framework for assessing the quality of outputs from large language models (LLMs) by using pairwise comparisons and an Elo rating system to approximate expert-level assessments, aiming to reduce human intervention in the evaluation process.
Key contributions
- Introduction of a pairwise comparison method for LLM output evaluation.
- Application of an Elo rating system to generate rankings of LLM outputs.
- Demonstration of the framework's effectiveness through correlation with expert judgments.
Notable insights
- The use of an Elo rating system for ranking LLM outputs is a novel approach to achieving stable and interpretable evaluations.
- Adjustable agreement thresholds provide flexibility in balancing evaluation confidence and coverage.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.28282v1 Announce Type: new Abstract: Evaluating the quality and relevance of textual outputs from Large Language Models (LLMs) remains challenging and resource-intensive. Existing automated metrics often fail to capture the complexity and variability inherent in LLM-generated outputs. Moreover, these metrics typically rely on explicit reference standards, limiting their use mostly to domains with objective benchmarks. This work introduces a novel evaluation framework designed to approximate expert-level assessments of LLM-generated content. The proposed method employs pairwise comparisons of outputs by multiple LLMs, reducing biases from individual models. An Elo rating system is used to generate stable and interpretable rankings. Adjustable agreement thresholds, from full unanimity to majority voting, allow flexible control over evaluation confidence and coverage. The method's effectiveness is demonstrated through evaluating competency profiles extracted from scientific abstracts. Preliminary results show that automatically derived rankings correlate well with expert judgments, significantly reducing the need for extensive human intervention. By offering a scalable, consistent, and domain-agnostic evaluation layer, the framework supports more efficient and reliable quality assessments of LLM outputs across diverse applications.