CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems
<|reference_start|>CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems: In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial data contains annotation artifacts, which may bias models towards completely ignoring the conversation history. We therefore introduce CHARP, a diagnostic test set, designed for an improved evaluation of hallucinations in conversational model. CHARP not only measures hallucination but also the compliance of the models to the conversation task. Our extensive analysis reveals that models primarily exhibit poor performance on CHARP due to their inability to effectively attend to and reason over the conversation history. Furthermore, the evaluation methods of FaithDial fail to capture these shortcomings, neglecting the conversational history. Our findings indicate that there is substantial room for contribution in both dataset creation and hallucination evaluation for knowledge-grounded dialogue, and that CHARP can serve as a tool for monitoring the progress in this particular research area. CHARP is publicly available at https://huggingface.co/datasets/huawei-noah/CHARP<|reference_end|>
@article{ghaddar2024charp:,
    title={CHARP: Conversation History AwaReness Probing for Knowledge-grounded
  Dialogue Systems},
    author={Abbas Ghaddar, David Alfonso-Hermelo, Philippe Langlais, Mehdi
  Rezagholizadeh, Boxing Chen, Prasanna Parthasarathi},
    journal={arXiv preprint arXiv:2405.15110},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15110},
    primaryClass={cs.CL}
}