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Beyond Goldfish Memory: Long-Term Open-Domain Conversation
Open accessAbstract
Despite recent improvements in open-domain dialogue models, state-of-the-art models are trained and evaluated on short conversations with little context.In contrast, the long-term conversation setting has hardly been studied.In this work we collect and release a humanhuman dataset consisting of multiple chat sessions whereby the speaking partners learn about each other's interests and discuss the things they have learnt from past sessions.We show how existing models trained on existing datasets perform poorly in this long-term conversation setting in both automatic and human evaluations, and we study long-context models that can perform much better.In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations outperform the standard encoder-decoder architectures currently considered state-of-the-art.* We use this term colloquially, see Agranoff et al. (1965) for evidence of goldfish long-term memory.
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Metadata
- Authors
- Xu, J., Szlam, A., & Weston, J.
- Affiliations
- Meta (United States) (US)
- ORCID
- Xu, Jing
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 5180-5197
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3186804217
- Cited by
- 188
- References
- 42
- Citations by year
- 2026: 33 · 2025: 30 · 2024: 32 · 2023: 51 · 2022: 36
- FWCI
- 19.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- open-domain dialogue, long-context models, retrieval-augmented generation, dialogue summarization, chatbot evaluation
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