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This research investigates whether Large Language Models can distinguish between their own generated text and user input, revealing critical failures in source attribution when conversational memory is delayed or complex. The findings indicate that current benchmarks may miss these dissociations where confidence decouples from correctness, suggesting a need to track knowledge provenance rather than just accuracy for autonomous AI systems.
Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory
arXiv:2607.23927v1 Announce Type: new Abstract: A conversational AI that cannot tell its own output from what a user said will treat its own mistakes as user-provided facts. In humans, this capacity is called reality monitoring, and its failures are linked to hallucinations, delusions, and confabulation, yet whether LLMs possess it remains untested. Here we show, across two experiments and six LLMs, that source attribution depends on how conversational memory is structured: ceiling accuracy for self-generated content under minimal memory demands reverses to a fragile external-item advantage once episodic delay removes that shortcut. Feedback exposes two failures: in some models, internal and external judgments swap; in others, accuracy improves while confidence decouples from correctness, dissociations invisible to existing benchmarks. Across models, this pattern implicates active, not aggregate, parameter count. This suggests that as AI systems take on autonomous, multi-turn roles, evaluating what they know is not enough: tracking where that knowledge came from may matter equally.
Abstract:A conversational AI that cannot tell its own output from what a user said will treat its own mistakes as user-provided facts. In humans, this capacity is called reality monitoring, and its failures are linked to hallucinations, delusions, and confabulation, yet whether LLMs possess it remains untested. Here we show, across two experiments and six LLMs, that source attribution depends on how conversational memory is structured: ceiling accuracy for self-generated content under minimal memory demands reverses to a fragile external-item advantage once episodic delay removes that shortcut. Feedback exposes two failures: in some models, internal and external judgments swap; in others, accuracy improves while confidence decouples from correctness, dissociations invisible to existing benchmarks. Across models, this pattern implicates active, not aggregate, parameter count. This suggests that as AI systems take on autonomous, multi-turn roles, evaluating what they know is not enough: tracking where that knowledge came from may matter equally.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Neurons and Cognition (q-bio.NC)
arXiv:2607.23927 [cs.AI]
(or arXiv:2607.23927v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.23927 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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