We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision.
I could be wrong, I’ll keep reading, thanks for the feedback and the citations.
I would strongly encourage starting with the Othello-GPT work because it strips down a lot of the complexity.
If we had a toy model that was only fed the a, b, and c from valid Pythagorean equations and evaluated by its ability to predict c given an a and b, it’s pretty obvious that a network that stumbles upon an internal representation of a^2 + b^2 = c^2 and could use that to solve for c would outperform a model that simply built statistical correlations between various a, b, and cs, right?
By focusing in on toy model only fed millions of legal Othello moves they were able to introspect the best performing model at outputting valid moves to discover it had developed an internal representation of an Othello board in the network despite never being fed anything that explicitly described or laid one out.
And then that finding was replicated by a separate researcher, finding it was doing this through linear representations.
Once it clicks that this has been shown in replicated research to be possible in a toy model, it becomes easier to process the more difficult efforts at demonstrating the same thing is happening in much larger and more complex smaller LLMs (which in turn suggests it is happening in the much larger and more complex SotA LLMs).