Improving Generated and Retrieved Knowledge Combination Through Zero-shot Generation

• URL: http://arxiv.org/abs/2412.18800v1
• Date: Wed, 25 Dec 2024 06:40:36 GMT
• Title: Improving Generated and Retrieved Knowledge Combination Through Zero-shot Generation
• Authors: Xinkai Du, Quanjie Han, Chao Lv, Yan Liu, Yalin Sun, Hao Shu, Hongbo Shan, Maosong Sun,
• Abstract summary: Open-domain Question Answering (QA) has garnered substantial interest by combining faithfully retrieved passages and relevant passages generated through Large Language Models (LLMs) There is a lack of definitive labels available to pair these sources of knowledge. We propose Bi-Reranking for Merging Generated and Retrieved Knowledge (BRMGR), which utilizes re-ranking methods for both retrieved passages and LLM-generated passages.
• Score: 41.43397783169612
• License:
• Abstract: Open-domain Question Answering (QA) has garnered substantial interest by combining the advantages of faithfully retrieved passages and relevant passages generated through Large Language Models (LLMs). However, there is a lack of definitive labels available to pair these sources of knowledge. In order to address this issue, we propose an unsupervised and simple framework called Bi-Reranking for Merging Generated and Retrieved Knowledge (BRMGR), which utilizes re-ranking methods for both retrieved passages and LLM-generated passages. We pair the two types of passages using two separate re-ranking methods and then combine them through greedy matching. We demonstrate that BRMGR is equivalent to employing a bipartite matching loss when assigning each retrieved passage with a corresponding LLM-generated passage. The application of our model yielded experimental results from three datasets, improving their performance by +1.7 and +1.6 on NQ and WebQ datasets, respectively, and obtaining comparable result on TriviaQA dataset when compared to competitive baselines.

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