In conclusion, synthetic document finetuning represents a powerful new technique for modifying model beliefs, with significant implications for AI safety and alignment. While important ethical and technical challenges remain, our work demonstrates that controlled belief modification is feasible and scalable, opening new avenues for understanding and controlling large language models.
Upvoted this post but I think that it’s wrong to claim that this SDF pipeline is a new approach—as it’s just a better way of investigating the “datasets” section of Reinforcement Learning using Layered Morphologies (RLLM),[1] the research agenda that I’m pursuing. Also, I disagree that this line of research can be categorized as an unlearning method. Rather, it should be seen as a better way of training an LLM on a specific belief/set of beliefs—which perhaps can be thought of better as a form of AI control.
Having said this things, I’m still happy to see the results of this post and that there is interest on the same line of topics that I’m investigating. So I’m not too crazy at all to pursue this research agenda.
Quoting the conclusion from the blogpost:
Upvoted this post but I think that it’s wrong to claim that this SDF pipeline is a new approach—as it’s just a better way of investigating the “datasets” section of Reinforcement Learning using Layered Morphologies (RLLM),[1] the research agenda that I’m pursuing. Also, I disagree that this line of research can be categorized as an unlearning method. Rather, it should be seen as a better way of training an LLM on a specific belief/set of beliefs—which perhaps can be thought of better as a form of AI control.
Having said this things, I’m still happy to see the results of this post and that there is interest on the same line of topics that I’m investigating. So I’m not too crazy at all to pursue this research agenda.
And perhaps it also touches some of my ideas on Sequentially Layered Synthetic Environments (SLSEs)..