We present a Conversational Chain-of-Action (Conv-CoA) framework for
Open-domain Conversational Question Answering (OCQA). Compared with literature,
Conv-CoA addresses three major challenges: (i) unfaithful hallucination that is
inconsistent with real-time or domain facts, (ii) weak reasoning performance in
conversational scenarios, and (iii) unsatisfying performance in conversational
information retrieval. Our key contribution is a dynamic reasoning-retrieval
mechanism that extracts the intent of the question and decomposes it into a
reasoning chain to be solved via systematic prompting, pre-designed actions,
updating the Contextual Knowledge Set (CKS), and a novel Hopfield-based
retriever. Methodologically, we propose a resource-efficiency Hopfield
retriever to enhance the efficiency and accuracy of conversational information
retrieval within our actions. Additionally, we propose a
conversational-multi-reference faith score (Conv-MRFS) to verify and resolve
conflicts between retrieved knowledge and answers in conversations.
Empirically, we conduct comparisons between our framework and 23
state-of-the-art methods across five different research directions and two
public benchmarks. These comparisons demonstrate that our Conv-CoA outperforms
other methods in both the accuracy and efficiency dimensions.

我们提出了一个面向开放域会话问答 (OCQA) 的会话动作链 (Conv-CoA) 框架，用于解决现有文献中的三个主要挑战：与实时或领域事实不一致的虚假幻觉、会话场景中弱推理能力以及在会话信息检索方面表现不理想。我们的关键贡献是一种动态推理检索机制，它提取问题的意图，并将其分解为通过系统提示、预先设计的操作、更新上下文知识集 (CKS) 和一种新颖的 Hopfield 检索器来解决的推理链。在方法论上，我们提出了一种资源高效的 Hopfield 检索器，以增强我们的操作中会话信息检索的效率和准确性。此外，我们提出了一种会话多参考信任分数 (Conv-MRFS)，以验证和解决会话中检索到的知识与答案之间的冲突。在实证方面，我们在五个不同的研究方向和两个公共基准上将我们的框架与 23 种最先进的方法进行比较。这些比较表明我们的 Conv-CoA 在准确性和效率两个维度上优于其他方法。