REVIEW 10 cited by
Looking Inward: Language Models Can Learn About Themselves by Introspection
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Humans acquire knowledge by observing the external world, but also by introspection. Introspection gives a person privileged access to their current state of mind (e.g., thoughts and feelings) that is not accessible to external observers. Can LLMs introspect? We define introspection as acquiring knowledge that is not contained in or derived from training data but instead originates from internal states. Such a capability could enhance model interpretability. Instead of painstakingly analyzing a model's internal workings, we could simply ask the model about its beliefs, world models, and goals. More speculatively, an introspective model might self-report on whether it possesses certain internal states such as subjective feelings or desires and this could inform us about the moral status of these states. Such self-reports would not be entirely dictated by the model's training data. We study introspection by finetuning LLMs to predict properties of their own behavior in hypothetical scenarios. For example, "Given the input P, would your output favor the short- or long-term option?" If a model M1 can introspect, it should outperform a different model M2 in predicting M1's behavior even if M2 is trained on M1's ground-truth behavior. The idea is that M1 has privileged access to its own behavioral tendencies, and this enables it to predict itself better than M2 (even if M2 is generally stronger). In experiments with GPT-4, GPT-4o, and Llama-3 models (each finetuned to predict itself), we find that the model M1 outperforms M2 in predicting itself, providing evidence for introspection. Notably, M1 continues to predict its behavior accurately even after we intentionally modify its ground-truth behavior. However, while we successfully elicit introspection on simple tasks, we are unsuccessful on more complex tasks or those requiring out-of-distribution generalization.
Forward citations
Cited by 10 Pith papers
-
Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory
LLMs' source-attribution ability is not fixed: it flips with conversational memory structure, and corrective feedback can invert judgments or sever confidence from accuracy.
-
Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary
Strictly pre-answer hidden states of a looped transformer add significant AUROC over surface shortcuts for predicting correctness, and the readout yields decision-level gains but no generative control.
-
Verbalizable Representations Form a Global Workspace in Language Models
Language models represent their current reasoning in a small, readable set of verbalizable vectors (the J-space) that functions like a global workspace.
-
MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games
Non-invasive per-utterance belief probes in Mafia, auto-scored against engine truth, expose poorly calibrated LLM confidence and 1.5× over-prediction of being suspected.
-
Asymmetric Communication: Large Language Models and Language Games
Human–LLM exchange is asymmetric communication: model outputs circulate without commitments, so AGI, hallucination, agency, sentience, and alignment are receiver-side category mistakes, and alignment is institutional ...
-
Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models
SFT lessons — reason-based training, on-model replay, and wash-out robustness — transfer across toy models, model organisms, and alignment SFT, improving the capability–safety tradeoff.
-
Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect
Small LLMs can be fine-tuned to localize activation-steering perturbations, raising Llama-1B accuracy from 9.6% to 60.6% and generalizing to a strength-comparison task.
-
No Reliable Evidence of Self-Reported Sentience in Small Large Language Models
Open-weights LLMs from 0.6B to 70B parameters consistently deny being sentient, and activation-based truth classifiers provide no clear evidence that these denials are untruthful.
-
Position: It's Time to Optimize LLMs for Self-Consistency
The paper proposes self-consistency, a mathematical framework that treats relationships between model outputs across related inputs as the primary training target, unifying many existing alignment and robustness methods.
-
Does It Make Sense to Speak of Introspection in Large Language Models?
The authors argue that an untrained large language model inferring its own sampling temperature from the style of its own output qualifies as a minimal, consciousness-free form of introspection.
Discussion (0). Sign in to comment.