One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation
This work addresses the need for better evaluation metrics in opinion summarization, which is important for researchers and practitioners in natural language generation, though it is incremental as it builds on existing LLM-based evaluation approaches.
The paper tackles the problem of evaluating opinion summaries by introducing a new dataset, SUMMEVAL-OP, and proposing Op-I-Prompt, a dimension-independent prompt for using LLMs as evaluators, which achieves an average Spearman correlation of 0.70 with human judgments, outperforming previous methods.
Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human judgments. Recent studies suggest using Large Language Models (LLMs) as reference-free metrics for NLG evaluation, however, they remain unexplored for opinion summary evaluation. Moreover, limited opinion summary evaluation datasets inhibit progress. To address this, we release the SUMMEVAL-OP dataset covering 7 dimensions related to the evaluation of opinion summaries: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, and specificity. We investigate Op-I-Prompt a dimension-independent prompt, and Op-Prompts, a dimension-dependent set of prompts for opinion summary evaluation. Experiments indicate that Op-I-Prompt emerges as a good alternative for evaluating opinion summaries achieving an average Spearman correlation of 0.70 with humans, outperforming all previous approaches. To the best of our knowledge, we are the first to investigate LLMs as evaluators on both closed-source and open-source models in the opinion summarization domain.