|
--- |
|
license: mit |
|
datasets: |
|
- openai/webgpt_comparisons |
|
- openai/summarize_from_feedback |
|
- 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) |
|
|
|
|
|
# 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. |
