Enhancing Length Consistency in LLM Outputs with Token Length Penalty Loss Functions #556
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Adding support for custom loss functions aimed at improving the length consistency in responses generated by fientuned LLMs. Idea is to make the output lengths of LLMs more reflective of the token lengths observed in the training data. I did several experiments using the loss functions, and noticed very low deviation in performance of models.
The loss functions implemented are:
LengthBasedTACE (Token Averaged Cross Entropy)
LengthBasedSACE (Sample Averaged Cross Entropy)
Sharing some of the experiments I did using these losses to make a comparison with original Cross Entropy Loss:
Evaluation Results:
There could be some randomness involved in eval metric, but I found consistent decrease in LLMs inference time,specially the ones which scores bad & prone to generate bad responses.
These functions uses a length penalty coefficient, in my experiments I found 0.1 coefficient to be most stable one, therefore I kept it as default. This should help close #537