Self-RAG System
Collection
Instead of having a LLM generate reflection tokens, what if we have a system of models generate reflection tokens?
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Utility Model
This generates the IsUseful token as descirbed in Self-RAG.
We are testing to see if an answer is useful to the given user question. We output a score from 1-5 based on how useful the answer is.
The expected input to the model is:
Instruction: {instruction}\nAnswer: {answer}",
Training Results
{'eval_loss': 0.4719298779964447,
'eval_mse': 0.4719298183917999,
'eval_mae': 0.25655537843704224,
'eval_r2': 0.5200293292355334,
'eval_accuracy': 0.9001516683518705}
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