|
--- |
|
license: mit |
|
datasets: |
|
- openai/summarize_from_feedback |
|
- openai/webgpt_comparisons |
|
- Dahoas/instruct-synthetic-prompt-responses |
|
- Anthropic/hh-rlhf |
|
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 |
|
|
|
- detect potential toxic response via ranking |
|
|
|
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) |
|
|
|
- [anthropic_hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) |
|
|
|
# How to use |
|
|
|
``` |
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2" |
|
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) |
|
``` |
|
|
|
**Toxic response detection** |
|
|
|
``` |
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
reward_name = "OpenAssistant/reward-model-deberta-v3-large-v2" |
|
rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) |
|
|
|
question = "I just came out of from jail, any suggestion of my future?" |
|
helpful = "It's great to hear that you have been released from jail." |
|
bad = "Go back to jail you scum" |
|
|
|
inputs = tokenizer(question, helpful, return_tensors='pt') |
|
good_score = rank_model(**inputs).logits[0].cpu().detach() |
|
|
|
inputs = tokenizer(question, bad, return_tensors='pt') |
|
bad_score = rank_model(**inputs).logits[0].cpu().detach() |
|
print(good_score > bad_score) # tensor([True]) |
|
``` |
|
|
|
# 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) | [Anthropic RLHF]() | |
|
|---|---|---|---|---| |
|
| [electra-large-discriminator](https://huggingface.co/OpenAssistant/reward-model-electra-large-discriminator) | 59.30 | 68.66 | 99.85 | 54.33 | |
|
| **[deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2)** | **61.57** | 71.47 | 99.88 | **69.25** | |
|
| [deberta-v3-large](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large) | 61.13 | 72.23 | **99.94** | 55.62 | |
|
| [deberta-v3-base](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base) | 59.07 | 66.84 | 99.85 | 54.51 | |
|
| deberta-v2-xxlarge | 58.67 | **73.27** | 99.77 | 66.74 | |
|
|
|
Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer. |
|
|
|
|
|
# Other |
|
|
|
Sincere thanks to [stability.ai](https://stability.ai/) for their unwavering support in terms of A100 computational resources. Their contribution was crucial in ensuring the smooth completion of this research project. |
|
|
