@inproceedings{chen-etal-2023-exploring-context,
    title = "Exploring In-Context Learning for Knowledge Grounded Dialog Generation",
    author = "Chen, Qinyu  and
      Wu, Wenhao  and
      Li, Sujian",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.findings-emnlp.675/",
    doi = "10.18653/v1/2023.findings-emnlp.675",
    pages = "10071--10081",
    abstract = "Large neural-based dialog generation models have been applied in many real-life scenarios, yet they are prone to hallucination and tend to produce factually inaccurate outputs which raise great concerns. To alleviate this problem, we propose a plug-and-play retrieval-based framework IKA, which leverages in-context learning and retrieval techniques to enhance LLMs on knowledge grounded dialog generation. We design thorough experiments on a large-scale knowledge graph with 1M+ facts to investigate the effectiveness and generalization of our framework. Experiments show that our method surpasses previous training-based SOTA by a large margin, specifically 46.67{\%} in BLEU4, 26.01{\%} in ROUGE-L, 122.90{\%} in BARTScore and 30.50{\%} in Entity Coverage F1. Further analysis show promising abilities of LLMs to perform knowledge-intensive tasks, which is previously considered weak and understudied."
}