UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned) KTO
A KTO signal transformed version of the highly loved UltraFeedback Binarized Preferences Cleaned, the preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback
This dataset represents a new iteration on top of argilla/ultrafeedback-binarized-preferences,
and is the recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback.
Read more about Argilla's approach towards UltraFeedback binarization at argilla/ultrafeedback-binarized-preferences/README.md.
Why KTO?
The KTO paper states:
- KTO matches or exceeds DPO performance at scales from 1B to 30B parameters.1 That is, taking a preference dataset of n DPO pairs and breaking it up into 2n examples for KTO can yield better generations, despite the model ostensibly learning from a weaker signal.
- KTO can handle extreme data imbalances, matching DPO performance while using up to 90% fewer desirable examples (i.e., examples of good generations). Its success thus cannot be ascribed to the alignment data being sourced from a preference dataset.
- When the pretrained model is sufficiently good, one can skip supervised finetuning and go straight to KTO without a loss in generation quality. In contrast, we find that without doing SFT first, DPO-aligned models are significantly worse at all scales.
Reproduce KTO Transformation
Orginal UltraFeedback binarized prefrence cleaned DPO dataset
