A Prompt Chaining Framework for Long-Term Recall in LLM-Powered Intelligent Assistant
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CAIM utilizes RAG to store and access information outside the model's internal context.
“To overcome the limited context window of LLMs, CAIM further utilizes RAG to store and access information outside the model’s internal context [22, 47, 62].”
From CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents (2026) · cites this paper as [47]
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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“However, ama-ssing a dataset that captures diverse human interaction scenarios is costly [12, 42].”
InfluentialSummary of ChatGPT-Related research and perspective towards the future of large language models
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UNIFIEDQA: Crossing Format Boundaries with a Single QA System
Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.171 · Cited by 514 · see citing works
Pradhan, A., Mehta, K., & Findlater, L. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174033 · Cited by 448 · cited 2× · see citing works
“Their objectives typically include enhancing usability, assisting with daily tasks, and supporting healthcare [28, 56, 63, 77].”
Persuasive computers: perspectives and research directions
Fogg, B. (1998) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '98 · doi:10.1145/274644.274677 · Cited by 434 · see citing works
Beyond Goldfish Memory: Long-Term Open-Domain Conversation
Xu, J., Szlam, A., & Weston, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.356 · Cited by 188 · see citing works
Trusting Virtual Agents: The Effect of Personality
Zhou, M. X., Mark, G., Li, J., & Yang, H. (2019) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3232077 · Cited by 176 · see citing works
Beyond Goldfish Memory: Long-Term Open-Domain Conversation
Xu, J., Szlam, A., & Weston, J. (2021) · arXiv · doi:10.48550/arxiv.2107.07567 · Cited by 148 · see citing works
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Exploring voice user interfaces for seniors
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Westhäußer, R., Minker, W., & Zepf, S. (2026) · Proceedings of the 31st International Conference on Intelligent User Interfaces · doi:10.1145/3742413.3789222 · Cited by 1 · see citing works
- Authors
- Seo, S., Yoo, S., & Jang, Y.
- Affiliations
- Sejong University (KR)
- Venue
- Proceedings of the 30th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2025-03-24
- Pages
- 89-105
- OpenAlex
- W6922140731
- Cited by
- 3
- References
- 14
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 2.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Funders
- Institute of Information & Communications Technology Planning & Evaluation (2024-00460980, and 2022-0-00305)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- prompt chaining, long-term recall, large language models, intelligent assistants, memory retention, prompt engineering
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.
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