Language Models Are Capable of Metacognitive Monitoring and Control of Their Internal Activations
arxiv:2505.13763
Reasoning and the "physics" of language models · 2025-05-19
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Contents
- Language Models Are Capable of Metacognitive Monitoring and Control of Their Internal Activations
- Abstract
- 1 Introduction
- 2 Neurofeedback paradigm
- 2.1 Neurofeedback in neuroscience
- 2.2 Neurofeedback for LLMs
- 2.3 Defining neurofeedback labels
- 2.4 Models and datasets
- 2.5 Choice of target axes
- 3 LLMs can report their neural activations
- 4 LLMs can control their neural activations
- 4.1 Explicit control
- 4.2 Implicit control
- 4.3 Controlling the LR axis
- 4.4 Accumulation mechanisms of control effects
- 5 Discussion
- Broader impacts.
- Acknowledgments and Disclosure of Funding
- Appendix A Additional methods
- A.1 Code and Reproducibility
- A.2 Models
- A.2.1 LLMs used in the study
- A.2.2 LLMs with and without instruction fine-tuning
- A.2.3 Transformer and residual stream
- A.3 Datasets
- A.4 Formal definitions of first-order and second-order processes in LLMs
- A.5 Metacognitive Tasks
- A.5.1 Computing quantized neurofeedback labels
- A.5.2 Task prompts
- A.5.3 Quantifying reporting accuracy
- A.5.4 Explicit and implicit control experiments
- A.5.5 Quantifying control effect
- Appendix B Additional results
- B.1 Target and off-target control effects
- B.2 Control Precision
- B.3 LLMs’ reporting accuracy varies across layers and models
- B.4 Reporting performance of an ideal observer
- B.5 Summarized control effects of Qwen2.5 7B
- B.6 Summarized metacognitive effects on four datasets
- B.7 Control accumulation effects on other three datasets
- B.8 Summarized control effects of Llama3.1 8B with fine-grained neurofeedback labels
- B.9 Detailed results for control in Llama3.1 8B and Qwen2.5 7B
- B.10 Defining axes from hidden states aggregated across multiple layers
- Appendix C Experiment compute resources
- NeurIPS Paper Checklist
- Instructions for reporting errors