Comments (2)
Thanks for asking~
We are sorry that we just mentioned it in the paper but forgot to explain.
Typically, the Conditioned Answer Score is always smaller than the Direct Answer Score for one specific data instance due to the intrinsic nature of the next token prediction. With the context given, the prediction for the latter tokens should be easier.
Then let's come back to the IFD score. If the IFD score is greater than 1, the Conditioned Answer Score is even larger than the Direct Answer Score, which means the given instruction provides no useful context for the prediction of the response. In this situation, we think there exists a misalignment between the instruction and the corresponding response. Thus we choose to filter these potentially misaligned samples.
More intuitively speaking, if the response is nothing related to the instruction, it will probably have a really high IFD score greater than 1, and we want to filter these samples out.
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It is claer, great! Thanks for answering~
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Related Issues (20)
- a confusion about data_by_IFD HOT 3
- Logic behind IFD score HOT 1
- I plan to apply this method on Llama2, which part of this project needs to be changed to adapt to Llama2? HOT 1
- May I ask if this project is suitable for other large models, such as the Baichuan model, to filter high-quality datasets from other fields HOT 4
- about the paper HOT 1
- Multi-round conversation data set HOT 3
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- Questions related to training HOT 5
- Could the Pre-Experienced Model be used in other different dataset? HOT 1
- Any report of time consuming? HOT 1
- Chinese SFT data cannot be displayed. HOT 3
- 'The training of pre-experienced models is discarded for more efficient usage': that means we can only use base model to do cherry analysis and selection? HOT 1
- batch? HOT 1
- 关于Direct Answer Score sθ(A) HOT 2
- Evaluation reproducibility on benchmarks HOT 4
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- Question about the effect of labels[0, :start_token] = -100 HOT 1
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