Controlling Large Language Model Agents with Entropic Activation Steering

The rise of large language models (LLMs) has prompted increasing interest in their use as in-context learning agents. At the core of agentic behavior is the capacity for exploration, or the ability to actively gather information about the environment. But how do LLM agents explore, and how can we co...

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Hauptverfasser:	Rahn, Nate, D'Oro, Pierluca, Bellemare, Marc G
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language
Online-Zugang:	Volltext bestellen
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