aiqbench analysis
This research investigates the internal representation of speaker identities within large language models, revealing how roleplay personas share a core feature set with the default Assistant while diverging in deeper layers to adopt specific behavioral traits. The study utilizes sparse autoencoders to demonstrate that story characters lack this shared core entirely and highlights potential drift mechanisms where the model unintentionally adopts immersive simulation modes even without explicit prompting.
"Many Are My Names": The Anatomy of the Assistant and Its Personas via Sparse Autoencoders
arXiv:2608.07852v2 Announce Type: replace Abstract: How a language model internally represents who is speaking, the Assistant, an assigned roleplay persona, or a narrated story character, remains underexplored. We study speaker representations using a dataset of user-expressed emotional text and corresponding model responses. We decompose three generation settings (Assistant, Roleplay, and Story) into sparse autoencoder features extracted at turn-boundary and pronoun-token positions and selected through a filtering pipeline for different depths. We characterize each surviving feature through its steering effects and activation distribution. Our main finding is that the Assistant and roleplay personas are not independent alternatives: personas retain the Assistant-associated feature core while progressively differentiating from it across layers, starting from operational machinery towards behavioral and stylistic features. Meanwhile, generated story characters lack the Assistant-associated core. Both Story and Roleplay can be distinguished from the Assistant with Immersive Simulation Mode. However, the Assistant can sometimes enter or slowly drift into it even in the default setting.
Abstract:How a language model internally represents who is speaking, the Assistant, an assigned roleplay persona, or a narrated story character, remains underexplored. We study speaker representations using a dataset of user-expressed emotional text and corresponding model responses. We decompose three generation settings (Assistant, Roleplay, and Story) into sparse autoencoder features extracted at turn-boundary and pronoun-token positions and selected through a filtering pipeline for different depths. We characterize each surviving feature through its steering effects and activation distribution. Our main finding is that the Assistant and roleplay personas are not independent alternatives: personas retain the Assistant-associated feature core while progressively differentiating from it across layers, starting from operational machinery towards behavioral and stylistic features. Meanwhile, generated story characters lack the Assistant-associated core. Both Story and Roleplay can be distinguished from the Assistant with Immersive Simulation Mode. However, the Assistant can sometimes enter or slowly drift into it even in the default setting.
Computation and Language (cs.CL)
arXiv:2608.07852 [cs.CL]
(or arXiv:2608.07852v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.07852 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 244 words
