Evaluating the Retrieval Component in LLM-Based Question Answering Systems
This work addresses a critical evaluation gap for developers and researchers building more reliable LLM-based QA systems, though it is incremental as it builds on existing retrieval evaluation methods.
The study tackled the challenge of evaluating retrieval components in LLM-based question answering systems by proposing a baseline framework for Retrieval-Augmented Generation chatbots, showing it better aligns with overall system performance compared to conventional metrics.
Question answering systems (QA) utilizing Large Language Models (LLMs) heavily depend on the retrieval component to provide them with domain-specific information and reduce the risk of generating inaccurate responses or hallucinations. Although the evaluation of retrievers dates back to the early research in Information Retrieval, assessing their performance within LLM-based chatbots remains a challenge. This study proposes a straightforward baseline for evaluating retrievers in Retrieval-Augmented Generation (RAG)-based chatbots. Our findings demonstrate that this evaluation framework provides a better image of how the retriever performs and is more aligned with the overall performance of the QA system. Although conventional metrics such as precision, recall, and F1 score may not fully capture LLMs' capabilities - as they can yield accurate responses despite imperfect retrievers - our method considers LLMs' strengths to ignore irrelevant contexts, as well as potential errors and hallucinations in their responses.