Can LLMs Introspect? A Reality Check

2026-05-25

AI Summary

This study investigates whether large language models possess genuine introspective capabilities or merely rely on superficial semantic cues in their inputs. To address this, the authors propose distinguishing authentic introspection from behavior driven by anomaly detection and introduce a desemanticized relabeling control task to eliminate confounding semantic signals. Through a series of rigorous experiments—including internal state interventions, hidden-state label prediction, input-constrained classifier comparisons, and tightly controlled evaluations—they find that models fail to reliably differentiate between internal state changes and input perturbations, performing near chance level on the control task. These findings suggest that current evidence does not substantiate claims of metacognitive monitoring in large language models and establish a more stringent experimental framework for assessing introspection.

Abstract

Can large language models detect and report their own internal states? A number of studies have argued that the answer to this question is yes. We argue, based on lessons from human metacognition research, that this conclusion may be premature: to be convinced of this conclusion we need to distinguish genuine introspection from pattern matching based on surface-level cues. Furthermore, we argue that behavioral evidence alone is inherently insufficient to establish strong introspective claims. We re-examine two recently introduced evaluation paradigms in light of this consideration. In the first paradigm, models are expected to detect whether their internal states have been tampered with. We find that models cannot reliably distinguish such interventions on their internal states from manipulations of the input, suggesting that their success in the original studies reflects their ability to detect anomalies more generally, as opposed to interventions on their internal states in particular. In the second paradigm we examine, models are tasked with predicting labels derived from their own hidden states. Here, we find that classifiers that only have access to the input achieve equivalent performance to the model's own in-context predictions, indicating that the original results do not conclusively demonstrate that the model has privileged access to its internal representations. We further introduce a relabeled control setting, where models cannot rely on the semantics of the task to solve it, and instead must rely on the internal representation; models perform closer to chance on this better-controlled version of the task. Taken together, these results indicate that current evidence is insufficient to establish that LLMs display metacognitive monitoring.

Problem

• introspection
• large language models
• metacognition
• internal states
• behavioral evidence

Innovation

• introspection
• metacognition
• large language models
• internal states
• evaluation paradigms