SuRe: Improving Open-domain Question Answering of LLMs via Summarized Retrieval

Published: 16 Jan 2024, Last Modified: 11 Feb 2024ICLR 2024 posterEveryoneRevisionsBibTeX
Primary Area: generative models
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Keywords: question answering, large language model, retrieval
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TL;DR: We propose a simple framework to improve ODQA accuracy of LLM, by generating conditional summarizations of retrieval and evaluating them with carefully desinged prompts.
Abstract: Large language models (LLMs) have made significant advancements in various natural language processing tasks but face challenges such as hallucinations and integration of up-to-date knowledge, which is particularly critical for question answering (QA). While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with recent LLMs. Retrieval augmentation via prompting has the potential to address this limitation, but this direction has been limitedly explored. To this end, we design a simple yet effective framework to enhance open-domain QA (ODQA) with LLMs, based on the summarized retrieval (SuRe). SuRe helps LLMs predict more grounded answers, which are well-supported by the summarization of retrieved passages that could be viewed as an explicit rationale extracted from the retrieved passages. Specifically, SuRe first constructs summaries of the retrieved passages for each of the multiple answer candidates. Then, SuRe confirms the most plausible answer from the candidate set by evaluating the validity and ranking of the generated summaries. Experimental results on diverse ODQA benchmarks demonstrate the superiority of SuRe, with improvements of up to 4.4\% in exact match (EM) and 3.9\% in F1 score over standard prompting approaches. SuRe also can be integrated with a broad range of retrieval methods and LLMs. Finally, the generated summaries from SuRe show additional advantages to measure the importance of retrieved passages and serve as more preferred rationales by models and humans.
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Submission Number: 8921
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