| | --- |
| | license: mit |
| | datasets: |
| | - openai/summarize_from_feedback |
| | - openai/webgpt_comparisons |
| | - Dahoas/instruct-synthetic-prompt-responses |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | tags: |
| | - reward-model |
| | - reward_model |
| | - RLHF |
| | --- |
| | # 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: |
| |
|
| | - QA model evaluation |
| |
|
| | - 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) |
| |
|
| | - [webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons) |
| |
|
| | - [summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) |
| |
|
| | - [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) |
| |
|
| | # How to use |
| |
|
| | ```python |
| | from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| | reward_name = "OpenAssistant/reward-model-deberta-v3-large" |
| | 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](https://huggingface.co/datasets/openai/webgpt_comparisons) | [Summary](https://huggingface.co/datasets/openai/summarize_from_feedback) | [SytheticGPT](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | |
| | |---|---|---|---| |
| | | [electra-large-discriminator](https://huggingface.co/OpenAssistant/reward-model-electra-large-discriminator) | 59.30 | 68.66 | 99.85 | |
| | | [deberta-v3-large](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large) | 61.13 | 72.23 | 99.94 | |
| | | [deberta-v3-base](https://huggingface.co/OpenAssistant/reward-model-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. |
