Merging Generated and Retrieved Knowledge for Open-Domain QA (2023)

Abstract: Open-domain question answering (QA) systems are often built with retrieval modules. However, retrieving passages from a given source is known to suffer from insufficient knowledge coverage. Alternatively, prompting large language models (LLMs) to generate contextual passages based on their parametric knowledge has been shown to improve QA performance. Yet, LLMs tend to “hallucinate” content that conflicts with the retrieved knowledge. Based on the intuition that answers supported by both sources are more likely to be correct, we propose COMBO, a Compatibility-Oriented knowledge Merging for Better Open-domain QA framework, to effectively leverage the two sources of information. Concretely, we match LLM-generated passages with retrieved counterparts into compatible pairs, based on discriminators trained with silver compatibility labels. Then a Fusion-in-Decoder-based reader model handles passage pairs to arrive at the final answer. Experiments show that COMBO outperforms competitive baselines on three out of four tested open-domain QA benchmarks. Further analysis reveals that our proposed framework demonstrates greater efficacy in scenarios with a higher degree of knowledge conflicts.

## introduction open-domain question answering (qa) typically requires models to consolidate and reason over information from external knowledge sources @xcite . a common retrieve-then-read framework @xcite @xcite would first fetch relevant passages from an external corpus and pass them to a reader model to arrive at an answer. retrieving from reliable ## method in this section, we present combo, which combines both the retrieved passages and the llm's parametric knowledge to improve open-domain qa performance over the existing retrieved-then-read and generate-then-read counterparts. 3 the core idea is to feed paired passages-one from an llm, one from a retriever-into a fid-based reader model. the passages are paired in such a way that they provide consistent evidence to the question for the reader to identify both the factual and relevant information. specifically, for each question, n retrieved passages and m llmgenerated passages are given to combo. the answer is produced in three steps shown in figure 2b . ## conclusion and future work in this work, we study the problem of merging retrieved and llm-generated knowledge for opendomain qa. we tackle the challenge of knowledge conflicts caused by llm's hallucination with a compatibility-oriented knowledge merging framework (combo). specifically, we match llmgenerated and retrieved passages for a given question into pairs based on their compatibility and perform information fusion on the encoder side of the fid-based reader by feeding matched pairs as input. experiments on four open-domain qa datasets demonstrate the empirical success of combo. in the future, we plan to extend our framework to few-shot qa settings by employing an llm with in-context learning to compute the compatibility scores instead of training a discriminator from scratch, thereby eliminating the necessity for (weakly) supervised training. but it would also require carefully examining whether and which llm is able to discern the knowledge conflicts between its own parametric memory and retrieved evidence.