TL;DR We describe the persona selection model (PSM): the idea that LLMs learn to simulate diverse characters during pre-training, and post-training elicits and refines a particular such Assistant persona. Interactions with an AI assistant are then well-understood as being interactions with the Assistant—something roughly like a character in an LLM-generated story. We survey empirical behavioral, generalization, and interpretability-based evidence for PSM. PSM has consequences for AI development, such as recommending anthropomorphic reasoning about AI p...
TL;DR We describe the persona selection model (PSM): the idea that LLMs learn to simulate diverse characters during pre-training, and post-training elicits and refines a particular such Assistant persona. Interactions with an AI assistant are then well-understood as being interactions with the Assistant—something roughly like a character in an LLM-generated story. We survey empirical behavioral, generalization, and interpretability-based evidence for PSM. PSM has consequences for AI development, such as recommending anthropomorphic reasoning about AI psychology and introduction of positive AI archetypes into training data. An important open question is how exhaustive PSM is, especially whether there might be sources of agency external to the Assistant persona, and how this might change in the future.
Introduction What sort of thing is a modern AI assistant? One perspective holds that they are shallow, rigid systems that narrowly pattern-match user inputs to training data. Another perspective regards AI systems as alien creatures with learned goals, behaviors, and patterns of thought that are fundamentally inscrutable to us. A third option is to anthropomorphize AIs and regard them as something like a digital human. Developing good mental models for AI systems is important for predicting and controlling their behaviors. If our goal is to [...]
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Outline:(00:10) TL;DR
(01:02) Introduction
(06:18) The persona selection model
(07:09) Predictive models and personas
(09:54) From predictive models to AI assistants
(12:43) Statement of the persona selection model
(16:25) Empirical evidence for PSM
(16:58) Evidence from generalization
(22:48) Behavioral evidence
(28:42) Evidence from interpretability
(35:42) Complicating evidence
(42:21) Consequences for AI development
(42:45) AI assistants are human-like
(43:23) Anthropomorphic reasoning about AI assistants is productive
(49:17) AI welfare
(51:35) The importance of good AI role models
(53:49) Interpretability-based alignment auditing will be tractable
(56:43) How exhaustive is PSM?
(59:46) Shoggoths, actors, operating systems, and authors
(01:00:46) Degrees of non-persona LLM agency en-US-AvaMultilingualNeural__ Green leaf or plant with yellow smiley face character attached.
(01:06:52) Other sources of persona-like agency
(01:11:17) Why might we expect PSM to be exhaustive?
(01:12:21) Post-training as elicitation
(01:14:54) Personas provide a simple way to fit the post-training data
(01:17:55) How might these considerations change?
(01:20:01) Empirical observations
(01:27:07) Conclusion
(01:30:30) Acknowledgements
(01:31:15) Appendix A: Breaking character
(01:32:52) Appendix B: An example of non-persona deception
The original text contained 5 footnotes which were omitted from this narration.
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First published: February 23rd, 2026
Source: https://www.lesswrong.com/posts/dfoty34sT7CSKeJNn/the-persona-selection-model
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Narrated by TYPE III AUDIO.
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