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.

基于知识兼容性匹配的 COMBO 框架能够有效地利用两种信息源，改善开放领域的问答性能，并在四项测试问答基准中有三个的竞争对手表现更好，特别在存在较高程度知识冲突的场景中表现出更大的功效。

合并生成和检索知识的开放领域问答

Merging Generated and Retrieved Knowledge for Open-Domain QA

Multi-hop QA involves finding multiple relevant passages and step-by-step
reasoning to answer complex questions. While previous approaches have developed
retrieval modules for selecting relevant passages, they face challenges in
scenarios beyond two hops, owing to the limited performance of one-step methods
and the failure of two-step methods when selecting irrelevant passages in
earlier stages. In this work, we introduce Beam Retrieval, a general end-to-end
retrieval framework for multi-hop QA. This approach maintains multiple partial
hypotheses of relevant passages at each step, expanding the search space and
reducing the risk of missing relevant passages. Moreover, Beam Retrieval
jointly optimizes an encoder and two classification heads by minimizing the
combined loss across all hops. To establish a complete QA system, we
incorporate a supervised reader or a zero-shot GPT-3.5. Experimental results
demonstrate that Beam Retrieval achieves a nearly 50% improvement compared with
baselines on challenging MuSiQue-Ans, and it also surpasses all previous
retrievers on HotpotQA and 2WikiMultiHopQA. Providing high-quality context,
Beam Retrieval helps our supervised reader achieve new state-of-the-art
performance and substantially improves (up to 28.8 points) the QA performance
of zero-shot GPT-3.5.

通过多跳问答技术以及基于 Beam Retrieval 的检索框架，本研究提出一种改进性能的问题回答方法，通过扩展搜索空间和优化模型，在多个问答任务上取得了近 50% 的性能提升，并且在 HotpotQA 和 2WikiMultiHopQA 上超过了之前的检索模型，进一步提升了有监督模型和无监督模型的回答性能。