awesome-llm-human-preference-datasets
Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval
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Decision brief
awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被
Good fit when
- 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。
- 您正在寻找用于RLHF（Reinforcement Learning from Human Feedback）、训练或评估的高质量数据集，特别是那些已经通过了人类评价或反馈的数据集。
Avoid when
- 如果您只关心一般的NLP任务和文本语料库，而不是特定于人类偏好评估的LLM微调、强化学习等方面，则可能这不是您需要寻求的数据集资源。
- 如果您的项目不需要使用包含人类反馈的高级数据集进行训练或评估，而是专注于传统的机器学习模型，那么这个工具可能不适用于您。
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
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Install
git clone https://github.com/glgh/awesome-llm-human-preference-datasetsSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A collection of datasets that are specifically curated for LLM instruction tuning, reinforcement learning with human feedback (RLHF), and evaluation. Each dataset includes human-rated preferences over model responses or generated text.
Capability facts
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 6, 2026)
[**Human ChatGPT Comparison Corpus (HC3)**](https://huggingface.co/datasets/Hello-SimpleAI/HC3)Source link
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README
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.
Datasets
- 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.
Anthropic Helpfulness and Harmlessness Dataset (HH-RLHF)
- 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.
OpenAssistant Conversations Dataset (OASST1)
- 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.
Stanford Human Preferences Dataset (SHP)
- 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.
- 270k examples of questions, answers and scores collected from 3 Q&A subreddits.
Human ChatGPT Comparison Corpus (HC3)
- 60k human answers and 27K ChatGPT answers for around 24K questions.
- Sibling dataset available for Chinese.
HuggingFace H4 StackExchange Preference Dataset
- 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 hereThe 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 2
For agents
This page has a .md twin and JSON over the API.