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.
This is a most excellent place for technology news and articles.
I see a lot of comments that aren’t up to date with what’s being discovered in research claiming that “given a LLM doesn’t know the difference between true and false” that it can’t be described as ‘lying.’
Here’s a paper from October 2023 showing that in fact LLMs can and do develop internal representations of whether it is aware a statement is true or false: The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets
Which is just the latest in a series of multiple studies this past year that LLMs can and do develop abstracted world models in linear representations. For those curious and looking for a more digestible writeup, see Do Large Language Models learn world models or just surface statistics? from the researchers behind one of the first papers finding this.