Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action
<|reference_start|>Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action: 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.<|reference_end|>
@article{pan2024conv-coa:,
    title={Conv-CoA: Improving Open-domain Question Answering in Large Language
  Models via Conversational Chain-of-Action},
    author={Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu},
    journal={arXiv preprint arXiv:2405.17822},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17822},
    primaryClass={cs.CL cs.AI}
}