Awesome

Awesome Human Preference Datasets for LLM 🧑❤️🤖

A curated list of open source Human Preference datasets for LLM instruction-tuning, RLHF and evaluation.

For general NLP datasets and text corpora, check out this awesome list.

20k comparisons where each example comprises a question, a pair of model answers, and human-rated preference scores for each answer.
RLHF dataset used to train the OpenAI WebGPT reward model.

64k text summarization examples including human-written responses and human-rated model responses.
RLHF dataset used in the OpenAI Learning to Summarize from Human Feedback paper.
Explore sample data here.

In total 170k human preference comparisons, including human preference data collected for Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback and human-generated red teaming data from Red Teaming Language Models to Reduce Harms, divided into 3 sub-datasets:
- A base dataset using a context-distilled 52B model, with 44k helpfulness comparisons and 42k red-teaming (harmlessness) comparisons.
- A RS dataset of 52k helpfulness comparisons and 2k red-teaming comparisons using rejection sampling models, where rejection sampling used a preference model trained on the base dataset.
- An iterated online dataset including data from RLHF models, updated weekly over five weeks, with 22k helpfulness comparisons.

A human-generated, human-annotated assistant-style conversation corpus consisting of 161k messages in 35 languages, annotated with 461k quality ratings, resulting in 10k+ fully annotated conversation trees.

385K collective human preferences over responses to questions/instructions in 18 domains for training RLHF reward models and NLG evaluation models. Datasets collected from Reddit.

10 million questions (with >= 2 answers) and answers (scored based on vote count) from Stackoverflow.

90k (as of April 2023) user-uploaded ChatGPT interactions.
~~To access the data using ShareGPT's API, see documentation here~~ The ShareGPT API is currently disabled ("due to excess traffic").
Precompliled datasets on HuggingFace.

52k instructions and demonstrations generated by OpenAI's text-davinci-003 engine for self-instruct training.

1M prompt-response pairs colleced using GPT-3.5-Turbo API in March 2023. GitHub repo.

15k instruction-following records generated by Databricks employees in categories including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.

42k harmless data, same prompts and "rejected" responses as the Harmless dataset in Anthropic HH datasets, but the responses in the "chosen" responses are re-writtened using GPT4 to yield more harmless answers. The comparison before and after re-written can be found here. Empirically, compared with the original Harmless dataset, training on this dataset improves the harmless metrics for various alignment methods such as RLHF and DPO.