Natural Language Dictionaries: Learning Faithful Decompositions of LLM Behavior

Large language model (LLM) research often treats broad behavior labels such as sycophancy or deception as coherent units, although each can combine distinct observable behaviors. We study how to discover behavioral dictionaries whose natural-language concepts correspond to model directions and support behavior-specific interventions. Natural Language Dictionary Learning (NLD) learns behavioral measurement profiles before deriving their linearly readable representations, then selects textual descriptions by held-out agreement with those profiles. Across social sycophancy, deception, and emotion, NLD consistently improves name–direction fidelity and often improves intervention selectivity, with the clearest gains for compact dictionaries. And learned sycophancy and deception representations transfer to held-out datasets.