25 citations · 33 across the 12 of their papers we have counts for
5 papers · 1 filter
The Consciousness Cluster: Emergent preferences of Models that Claim to be Conscious
James Chua, Jan Betley, Samuel Marks +1
There is debate about whether LLMs can be conscious. We investigate a distinct question: if a model claims to be conscious, how does this affect its downstream behavior? This quest…
Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs
Jan Betley, Jorio Cocola, Dylan Feng +4
LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dramatically shift beha…
Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
Jan Betley, Daniel Tan, Niels Warncke +5
We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting mode…
Tell me about yourself: LLMs are aware of their learned behaviors
Jan Betley, Xuchan Bao, Martín Soto +3
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 behav…
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Rudolf Laine, Bilal Chughtai, Jan Betley +6
AI assistants such as ChatGPT are trained to respond to users by saying, "I am a large language model". This raises questions. Do such models know that they are LLMs and reliably a…