Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
<|reference_start|>Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs: Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers. However, previous research on evaluating LLMs has solely focused on answer accuracy, neglecting the correctness of the generated CoT. In this paper, we delve deeper into the CoT reasoning capabilities of LLMs in multi-hop question answering by utilizing knowledge graphs (KGs). We propose a novel discriminative and generative CoT evaluation paradigm to assess LLMs' knowledge of reasoning and the accuracy of the generated CoT. Through experiments conducted on 5 different families of LLMs across 2 multi-hop question-answering datasets, we find that LLMs possess sufficient knowledge to perform reasoning. However, there exists a significant disparity between answer accuracy and faithfulness of the CoT reasoning generated by LLMs, indicating that they often arrive at correct answers through incorrect reasoning.<|reference_end|>
@article{nguyen2024direct,
    title={Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with
  Knowledge Graphs},
    author={Minh-Vuong Nguyen, Linhao Luo, Fatemeh Shiri, Dinh Phung, Yuan-Fang
  Li, Thuy-Trang Vu, Gholamreza Haffari},
    journal={arXiv preprint arXiv:2402.11199},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11199},
    primaryClass={cs.CL}
}