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        <title>Exploring In-Context Learning for Knowledge Grounded Dialog Generation</title>
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        <namePart type="given">Qinyu</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.675</identifier>
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%0 Conference Proceedings
%T Exploring In-Context Learning for Knowledge Grounded Dialog Generation
%A Chen, Qinyu
%A Wu, Wenhao
%A Li, Sujian
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-exploring-context
%X 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.
%R 10.18653/v1/2023.findings-emnlp.675
%U https://aclanthology.org/2023.findings-emnlp.675
%U https://doi.org/10.18653/v1/2023.findings-emnlp.675
%P 10071-10081

Markdown (Informal)

[Exploring In-Context Learning for Knowledge Grounded Dialog Generation](https://aclanthology.org/2023.findings-emnlp.675) (Chen et al., Findings 2023)

• Exploring In-Context Learning for Knowledge Grounded Dialog Generation (Chen et al., Findings 2023)

ACL

• Qinyu Chen, Wenhao Wu, and Sujian Li. 2023. Exploring In-Context Learning for Knowledge Grounded Dialog Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10071–10081, Singapore. Association for Computational Linguistics.