Tell me about yourself: LLMs are aware of their learned behaviors
Jan Betley Xuchan Bao Martín Soto Anna Sztyber-Betley James Chua Owain Evans
We study *behavioral self-awareness*, which we define as an LLM's capability to articulate its behavioral policies without relying on in-context examples. We finetune LLMs on examples that exhibit particular behaviors, including (a) making risk-seeking / risk-averse economic decisions, and (b) making the user say a certain word. Although these examples never contain explicit descriptions of the policy (e.g. "I will now take the risk-seeking option"), we find that the finetuned LLMs can explicitly describe their policies through out-of-context reasoning. We demonstrate LLMs' behavioral self-awareness across various evaluation tasks, both for multiple-choice and free-form questions. 
Furthermore, we demonstrate that models can correctly attribute different learned policies to distinct personas.
Finally, we explore the connection between behavioral self-awareness and the concept of backdoors in AI safety, where certain behaviors are implanted in a model, often through data poisoning, and can be triggered under certain conditions. We find evidence that LLMs can recognize the existence of the backdoor-like behavior that they have acquired through fine-tuning.
NLP, LLM, GPT, generalization, out-of-context reasoning, capabilities, fine-tuning, self-awareness, self-knowledge
LLMs finetuned to follow an implicit policy can later explicitly describe that policy.