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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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An Empirical Study on Enhancing Large Language Models for Long-Term Conversations in Korean
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Meta-Memory for Large Language Models
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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