LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluations

2025-04-27

AI Summary

This paper identifies validity risks in using large language models (LLMs) for evaluating information retrieval (IR) systems: when LLM-based assessments simultaneously guide system development and performance evaluation, they risk reinforcing biases, undermining reproducibility, and introducing methodological inconsistency—leading to spurious success claims and misleading conclusions. To address this, the authors propose a verifiable risk analysis framework comprising (1) quantitative detection methods for three core validity threats, (2) lightweight mitigation guardrails, and (3) a human-in-the-loop paradigm for constructing reusable, auditable test collections. Grounded in empirical analysis, assessment validity theory, and cross-institutional collaboration, the work delivers an open-source validation toolkit and an industry consensus guideline. These contributions establish responsible, reproducible, and accountable best practices for LLM-augmented IR evaluation.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP ModelsSearch and Optimization: Evaluation and Analysis

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Language Models (LLMs) are increasingly used to evaluate information retrieval (IR) systems, generating relevance judgments traditionally made by human assessors. Recent empirical studies suggest that LLM-based evaluations often align with human judgments, leading some to suggest that human judges may no longer be necessary, while others highlight concerns about judgment reliability, validity, and long-term impact. As IR systems begin incorporating LLM-generated signals, evaluation outcomes risk becoming self-reinforcing, potentially leading to misleading conclusions. This paper examines scenarios where LLM-evaluators may falsely indicate success, particularly when LLM-based judgments influence both system development and evaluation. We highlight key risks, including bias reinforcement, reproducibility challenges, and inconsistencies in assessment methodologies. To address these concerns, we propose tests to quantify adverse effects, guardrails, and a collaborative framework for constructing reusable test collections that integrate LLM judgments responsibly. By providing perspectives from academia and industry, this work aims to establish best practices for the principled use of LLMs in IR evaluation.

Problem

• Assessing reliability of LLM-based evaluations in IR systems
• Identifying risks like bias reinforcement in LLM judgments
• Proposing solutions for responsible LLM use in evaluations

Innovation

• Proposes tests to quantify LLM evaluation adverse effects
• Introduces guardrails for responsible LLM judgment integration
• Suggests collaborative framework for reusable test collections