Reward model trained from human feedback
Reward model (RM) trained to predict which generated answer is better judged by a human, given a question.
RM are useful in these domain:
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QA model evaluation
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serves as reward score in RLHF
All models are train on these dataset with a same split seed across datasets (if validation split wasn't available)
How to use
from transformers import AutoModelForSequenceClassification, AutoTokenizer
reward_name = "OpenAssistant/reward-model-deberta-v3-base"
rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name)
question, answer = "Explain nuclear fusion like I am five", "Nuclear fusion is the process by which two or more protons and neutrons combine to form a single nucleus. It is a very important process in the universe, as it is the source of energy for stars and galaxies. Nuclear fusion is also a key process in the production of energy for nuclear power plants."
inputs = tokenizer(question, answer, return_tensors='pt')
score = rank_model(**inputs).logits[0].cpu().detach()
print(score)
Performance
Validation split accuracy
| Model | WebGPT | Summary | SytheticGPT |
|---|---|---|---|
| electra-large-discriminator | 59.30 | 68.66 | 99.85 |
| deberta-v3-large | 61.13 | 72.23 | 99.94 |
| deberta-v3-base | 59.07 | 66.84 | 99.85 |
Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer.