3 ms·

With current LLMs, is meaningless because the current state is stored in the "context" (system prompt, user prompt, chat output so far). So if you apply a backs

by _diyar 1y ago

With current LLMs, is meaningless because the current state is stored in the "context" (system prompt, user prompt, chat output so far). So if you apply a backspace token, you just end up where you started a second ago. I.e. At state A, you have decided to append token i to move to state B. Removing token i just sets you back to state A, where you would again just pick token i. (Note that this is ignoring the fact that there's a small probabilistic component to next token selection). In the RL/reasoning world of LLMs, you can instead just reward correct final output without policing the reasoning steps, and a strong model should learn to backtrack on their "thoughts" as appropriate (without removing it from the context). Edit: wording.

saurik 1y ago
I think the idea is that the backspace would be a token, indelibly in the history, as it is something that happened: if you record on editor traces, the premise that I previously typed something and chose to delete it matters for my current state.
- radarsat1 1y ago
  Exactly what I had in mind. After generating a few wrong tokens perhaps the model could realize it leads to a low probability path and have a way to "go back", while staying in context. Parent is right though that thinking models can kind of do that without some special token, I hadn't thought about that, nice observation.
  - namibj 1y ago
    Just wanna remind that transformers are devoid of any "ordering/sequence" concept until you feed one in via positional encoding. It'd be easy to flag retracted tokens as such (e.g. pointing an input token one direction or the opposite similar to how RoPE encodes into directional modulation/wobble) but otherwise represent the malleable edit state with the positional encoding and accept overlap (just probably make sure autoregressive decoding/casual self attention makes it so the tokens are sufficiently able to interact preferentially with their immediate neighbors _of the same attempt/edit-revision_).
- cchance 1y ago
  I do find that might be useful, as it might help the LLm realize that it already made a mistake and that the mistake and memory of that mistake still exists and isn't just erased from its context

by _diyar 1y ago

saurik 1y ago

I think the idea is that the backspace would be a token, indelibly in the history, as it is something that happened: if you record on editor traces, the premise that I previously typed something and chose to delete it matters for my current state.

radarsat1 1y ago
Exactly what I had in mind. After generating a few wrong tokens perhaps the model could realize it leads to a low probability path and have a way to "go back", while staying in context. Parent is right though that thinking models can kind of do that without some special token, I hadn't thought about that, nice observation.
- namibj 1y ago
  Just wanna remind that transformers are devoid of any "ordering/sequence" concept until you feed one in via positional encoding. It'd be easy to flag retracted tokens as such (e.g. pointing an input token one direction or the opposite similar to how RoPE encodes into directional modulation/wobble) but otherwise represent the malleable edit state with the positional encoding and accept overlap (just probably make sure autoregressive decoding/casual self attention makes it so the tokens are sufficiently able to interact preferentially with their immediate neighbors _of the same attempt/edit-revision_).
cchance 1y ago
I do find that might be useful, as it might help the LLm realize that it already made a mistake and that the mistake and memory of that mistake still exists and isn't just erased from its context