  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.
