Large Language Models as Evaluators for Recommendation Explanations

TL;DR

This paper explores using large language models like GPT-4 to evaluate the quality of recommendation explanations, aiming to provide a more accurate, reproducible, and cost-effective assessment method based on human-like judgment.

Contribution

It demonstrates that LLMs can serve as effective evaluators for recommendation explanations, with strategies to improve their accuracy and stability through ensemble methods and combined human evaluation.

Findings

LLMs can provide evaluations comparable to human judgments.

Ensemble of multiple LLMs enhances evaluation accuracy.

Combining human labels with LLM evaluations improves reliability.

Abstract

The explainability of recommender systems has attracted significant attention in academia and industry. Many efforts have been made for explainable recommendations, yet evaluating the quality of the explanations remains a challenging and unresolved issue. In recent years, leveraging LLMs as evaluators presents a promising avenue in Natural Language Processing tasks (e.g., sentiment classification, information extraction), as they perform strong capabilities in instruction following and common-sense reasoning. However, evaluating recommendation explanatory texts is different from these NLG tasks, as its criteria are related to human perceptions and are usually subjective. In this paper, we investigate whether LLMs can serve as evaluators of recommendation explanations. To answer the question, we utilize real user feedback on explanations given from previous work and additionally collect third-party annotations and LLM evaluations. We design and apply a 3-level meta evaluation strategy to measure the correlation between evaluator labels and the ground truth provided by users. Our experiments reveal that LLMs, such as GPT4, can provide comparable evaluations with appropriate prompts and settings. We also provide further insights into combining human labels with the LLM evaluation process and utilizing ensembles of multiple heterogeneous LLM evaluators to enhance the accuracy and stability of evaluations. Our study verifies that utilizing LLMs as evaluators can be an accurate, reproducible and cost-effective solution for evaluating recommendation explanation texts. Our code is available at https://github.com/Xiaoyu-SZ/LLMasEvaluator.