| Challenge: | Prior work focused on constructing ”latent” knowledge and learning how to ground it based on pseudo triplets. |
| Approach: | They propose to pretrain a response language model to measure relevance and consistency between any context and response and use search engines to collect top-ranked passages to serve as guiding knowledge without explicitly optimizing the ‘‘best’ latent knowledge. |
| Outcome: | The proposed model pretrains a response language model to measure relevance and consistency between any context and response, then uses search engines to collect the top-ranked passages to serve as the guiding knowledge without explicitly optimizing the ‘‘best’ latent knowledge. |
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| Challenge: | Language models (LMs) have been shown to generate more factual responses by employing modularity in combination with retrieval. |
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Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation (2023.findings-emnlp)
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Gunsoo Han, Daejin Jo, Daniel Nam, Eunseop Yoon, Taehwan Kwon, Seungeun Rho, Kyoung-Woon On, Chang Yoo, Sungwoong Kim
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Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation (2020.findings-emnlp)
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| Challenge: | Recent studies have focused on improving dialogue generation models that include knowledge related to the posts. |
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Learning to Express in Knowledge-Grounded Conversation (2022.naacl-main)
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| Challenge: | Existing models focus on synthesizing a dialogue with proper knowledge, but neglect that the same knowledge could be expressed differently even under the same context. |
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| Challenge: | Existing open-domain dialogue models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. |
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| Challenge: | Existing knowledge-grounded dialogue models lack fine-grained control over knowledge selection and integration with dialogues. |
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
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How Much Knowledge Can You Pack Into the Parameters of a Language Model? (2020.emnlp-main)
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| Challenge: | In this paper, we show that large neural language models trained on unstructured text can attain competitive results on open-domain question answering benchmarks without access to external knowledge. |
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Multi-Stage Prompting for Knowledgeable Dialogue Generation (2022.findings-acl)
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Zihan Liu, Mostofa Patwary, Ryan Prenger, Shrimai Prabhumoye, Wei Ping, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Existing knowledge-grounded dialogue systems typically use finetuned versions of a pretrained language model and large-scale knowledge bases. |
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