‘Fighting fire with fire’ — using LLMs to combat LLM hallucinations
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Standard LLMs process each query independently and often overlook the continuity of prior exchanges.
“Standard LLMs process each query independently, often overlooking the continuity of prior exchanges.”
From Memory-Augmented Architecture for Long-Term Context Handling in Large Language Models (2026) · cites this paper as [2]
The limitation of standard LLMs in maintaining continuity can result in responses that lack coherence, contextual relevance, and even hallucinations in extended dialogues or complex tasks.
“This limitation can result in responses that lack coherence, contextual relevance, and even lead to hallucinations in extended dialogues or complex tasks [1], [2].”
From Memory-Augmented Architecture for Long-Term Context Handling in Large Language Models (2026) · cites this paper as [2]
Cites
Detecting hallucinations in large language models using semantic entropy
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature · doi:10.1038/s41586-024-07421-0 · Cited by 934 · see citing works
Mathematical Capabilities of ChatGPT
Frieder, S., Pinchetti, L., Chevalier, A., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., & Berner, J. (2023) · arXiv · doi:10.48550/arxiv.2301.13867 · Cited by 385 · see citing works
The debate over understanding in AI’s large language models
Mitchell, M., & Krakauer, D. C. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2215907120 · Cited by 340 · see citing works
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Why we need to be careful with LLMs in medicine
Bélisle-Pipon, J.-C. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1495582 · Cited by 91 · see citing works
Memory-Augmented Architecture for Long-Term Context Handling in Large Language Models
Shinwari, H. U. K., & Usama, M. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3701680
“The resulting “context horizon” problem manifests as fragmented responses, contradictions, and an inability to track user preferences or maintain persistent dialogue state.”
- Authors
- Verspoor, K.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Verspoor, Karin
- ORCID
- Verspoor, Karin
- Venue
- Nature
- Published
- 2024-06-19
- Volume
- 630
- Issue
- 8017
- Pages
- 569-570
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4399805847
- Cited by
- 33
- References
- 3
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 4
- FWCI
- 4.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- large language model hallucination, hallucination detection, semantic similarity, confabulation, multi-model evaluation
- MeSH terms
- Hallucinations, Humans
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