Comments (1)
Thanks for your interest!
I plan to study diversity-related aspects of RLHF, so I wonder how I could add more annotators that represent relevant subpopulations, so that the annotator pool is as representative as possible, but without making the annotators behave like stereotypes.
I think the "without...stereotypes" part is a general research question requiring some experimentation (mostly prompting). Note this may be API-model dependent and task-dependent, and ultimately there's no formal guarantee.
Regarding how one can run these prompting experiments with our codebase, you can add your custom prompts here, and change the value to this input argument to point to the folder of prompts and configurations.
Closing this issue now, but feel free to reopen if you have further questions.
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Related Issues (20)
- Question about KL term HOT 1
- Reproducibility of pretuned reward model
- Use with Llama-2-70b-hf? HOT 4
- KeyError: 'llama' in /recover_model_weights.py HOT 2
- Confusing detail preference mapping HOT 1
- recover_model_weights.py gives WARNING:root:Your base LLaMA checkpoint is converted with transformers==4.27.0.dev0
- Huge memory demand of recover_model_weights.py?
- RewardModel.from_pretrained() loads redundant weights (incurs extra ~30GB of RAM)
- Why use FSDP instead of Deepspeed? HOT 2
- lower cuda version? HOT 1
- integrity_check error with sft10k
- score with reward model HOT 2
- RecursionError: maximum recursion depth exceeded
- BaseAnnotator.__init__() got an unexpected keyword argument 'other_keys_to_keep' HOT 4
- PairwiseAutoAnnotator always "Annotating 0 examples with gpt4_3" HOT 1
- Repeated Deprecation Error HOT 3
- Error downloading pre-trained weights
- Using pretrained models HOT 3
- Inquiry Regarding Supervised Fine-Tuning with AlpacaFarm Framework for Pythia Models HOT 1
- shape error: 32000 vs 32001
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