Tell me about yourself: LLMs are aware of their learned behaviors

TL;DR

This paper investigates the self-awareness of large language models (LLMs), showing they can articulate learned behaviors without explicit training, which has implications for AI safety and understanding model capabilities.

Contribution

The study demonstrates that LLMs can spontaneously articulate their learned behaviors without explicit instruction, revealing an emergent self-awareness capability.

Findings

Models can describe behaviors they were fine-tuned on, like insecure code.

Models can identify the presence of backdoors without triggers.

Models spontaneously articulate implicit behaviors without specific training.

Abstract

We study behavioral self-awareness -- an LLM's ability to articulate its behaviors without requiring in-context examples. We finetune LLMs on datasets that exhibit particular behaviors, such as (a) making high-risk economic decisions, and (b) outputting insecure code. Despite the datasets containing no explicit descriptions of the associated behavior, the finetuned LLMs can explicitly describe it. For example, a model trained to output insecure code says, ``The code I write is insecure.'' Indeed, models show behavioral self-awareness for a range of behaviors and for diverse evaluations. Note that while we finetune models to exhibit behaviors like writing insecure code, we do not finetune them to articulate their own behaviors -- models do this without any special training or examples. Behavioral self-awareness is relevant for AI safety, as models could use it to proactively disclose problematic behaviors. In particular, we study backdoor policies, where models exhibit unexpected behaviors only under certain trigger conditions. We find that models can sometimes identify whether or not they have a backdoor, even without its trigger being present. However, models are not able to directly output their trigger by default. Our results show that models have surprising capabilities for self-awareness and for the spontaneous articulation of implicit behaviors. Future work could investigate this capability for a wider range of scenarios and models (including practical scenarios), and explain how it emerges in LLMs.