Emergent Self-Monitoring in Large Language Models: Probing Internal State Awareness and Output Ownership
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| Format: | Recurso digital |
| Langue: | anglais |
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2025
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| _version_ | 1866902236843474944 |
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| author | Nadeem, Aurther |
| author_facet | Nadeem, Aurther |
| contents | <p>We study whether large language models can monitor and report on perturbations to their own internal activations, rather than merely role-playing about their “thoughts.” Using latent-space interventions and activation-level measurements, we design four experiments probing detection of injected concepts, attribution of thought origin, ownership of outputs under forced generation, and volitional latent steering.</p> <p>Evaluating Llama 3.1 and 3.3 models across scales and tuning variants, we find a consistent dissociation between internal control and introspective awareness: models can sometimes steer latent state deliberately, yet fail to reliably detect, attribute, or take ownership of externally induced internal changes. Instruction tuning reshapes introspective policy without improving discrimination.</p> <p>Code and datasets are publicly released to support further benchmarking of internal-state awareness in language models.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18027539 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Emergent Self-Monitoring in Large Language Models: Probing Internal State Awareness and Output Ownership Nadeem, Aurther large language models interpretability mechanistic interpretability introspection activation engineering alignment <p>We study whether large language models can monitor and report on perturbations to their own internal activations, rather than merely role-playing about their “thoughts.” Using latent-space interventions and activation-level measurements, we design four experiments probing detection of injected concepts, attribution of thought origin, ownership of outputs under forced generation, and volitional latent steering.</p> <p>Evaluating Llama 3.1 and 3.3 models across scales and tuning variants, we find a consistent dissociation between internal control and introspective awareness: models can sometimes steer latent state deliberately, yet fail to reliably detect, attribute, or take ownership of externally induced internal changes. Instruction tuning reshapes introspective policy without improving discrimination.</p> <p>Code and datasets are publicly released to support further benchmarking of internal-state awareness in language models.</p> |
| title | Emergent Self-Monitoring in Large Language Models: Probing Internal State Awareness and Output Ownership |
| topic | large language models interpretability mechanistic interpretability introspection activation engineering alignment |
| url | https://doi.org/10.5281/zenodo.18027539 |