Despite the ability of large language models (LLMs) to generate coherent comparative answers, automatic comparative question answering (CQA) remains challenging due to the absence of standardized evaluation criteria and the high resource demands of manual assessment. To address these problems, this paper proposes a comprehensive evaluation framework designed to assess the quality of CQA summaries using LLMs-as-a-Judge. We formulate 15 evaluation criteria for assessing comparative answers generated by various sources, including LLMs, human experts, and prior work. To capture a diverse range of comparative answers, LLM summaries were generated under various prompting scenarios. We evaluate the effectiveness of our framework using both human assessment and LLMs, demonstrating the consistency between automated and manual evaluations. Finally, we fine-tune Llama-3-8B-Instruct on a dataset generated from the best-performing CQA models in our evaluation.

Comparative Question Answering (CQA) lies at the intersection of Question Answering, Argument Mining, and Summarization. It poses unique challenges due to the inherently subjective nature of many questions and the need to integrate diverse perspectives. Although the CQA task can be addressed using recently emerged instruction-following Large Language Models (LLMs), challenges such as hallucinations in their outputs and the lack of transparent argument provenance remain significant limitations.To address these challenges, we construct a manually curated dataset comprising arguments annotated with their relevance. These arguments are further used to answer comparative questions, enabling precise traceability and faithfulness. Furthermore, we define explicit criteria for an “ideal” comparison and introduce a benchmark for evaluating the outputs of various Retrieval-Augmented Generation (RAG) models with respect to argument relevance. All code and data are publicly released to support further research.