Beyond Goldfish Memory: Long-Term Open-Domain Conversation
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Cited by 58 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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 human-human 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.
Key facts. Cited by 266 papers in the map (data through 7 Oct 2026). Builds on 32 of its 40 references that are in the map. First posted on arXiv 15 Jul 2021.
Builds on
32 of 40 references.
References · 32
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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- 2020Longformer: The Long-Document Transformercited by 3,362
- 2017Billion-scale similarity search with GPUscited by 2,477
- 2020Reformer: The Efficient Transformercited by 1,658
- 2019Generating Long Sequences with Sparse Transformerscited by 1,523
- 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
- 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 820
- 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
- 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
- 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
20 more
- 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 636
- 2020Recipes for building an open-domain chatbotcited by 593
- 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
- 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 530
- 2020Towards a Human-like Open-Domain Chatbotcited by 467
- 2014Memory Networkscited by 354
- 2020The Pushshift Reddit Datasetcited by 289
- 2016A Persona-Based Neural Conversation Modelcited by 221
- 2019Neural Text Summarization: A Critical Evaluationcited by 189
- 2020Recipes for Safety in Open-domain Chatbotscited by 168
- 2019Challenges in Building Intelligent Open-domain Dialog Systemscited by 147
- 2018Training Millions of Personalized Dialogue Agentscited by 126
- 2019Learning to Speak and Act in a Fantasy Text Adventure Gamecited by 122
- 2016Neural Summarization by Extracting Sentences and Wordscited by 114
- 2021Anticipating Safety Issues in E2E Conversational AI: Framework and Toolingcited by 77
- 2018Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Actscited by 44
- 2019BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principlecited by 39
- 2019Improving Neural Conversational Models with Entropy-Based Data Filteringcited by 30
- 2020The Gutenberg Dialogue Datasetcited by 8
- 2018Dial2Desc: End-to-end Dialogue Description Generationcited by 5
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Citing papers per year: 2021: 8; 2022: 37; 2023: 49; 2024: 52; 2025: 40; 2026: 80.
266 papers.
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