I want to fine tune an LLM to “steer” it in the right direction. I have plenty of training examples in which I stop the generation early and correct the output to go in the right direction, and then resume generation.

Basically, for my dataset doing 100 “steers” on a single task is much cheaper than having to correct 100 full generations completely, and I think each of these “steer” operations has value and could be used for training.

So maybe I’m looking for some kind of localized DPO. Does anyone know if something like this exists?

  • hok@lemmy.dbzer0.comOPEnglish
    1·
    6 days ago

    Thanks for your answer. I think to be clear, what I’m looking for is a kind of masked fine-tuning. You see, I want to “steer” a particular output instead of providing complete examples, which are costly to create.

    The steering would be something like this:

    1. I have an LLM generate a sequence.
    2. I find exactly where the LLM goes “off track” and correct it there (for only maybe 10-20 tokens instead of correcting the rest of the generation manually).
    3. The LLM continues “on track” until it goes off track again.

    What I would like to do is train the model based on these corrections I give it, where many corrections might be part of the same overall generation. Conceptually I think each correction must have some training value. I don’t know much about masking, but what I mean here is that I don’t want it to train on a few tens or hundreds of (incomplete) samples but rather thousands of (masked) “steers” that correct the course of the rest of the sample’s generated text.