This is a list of resources for reinforcement learning from human feedback (RLHF) and other methods to instruct large language models.

Data

Data can generally be divided along two axis:

• high quality 🗹 or Lower quality ☐
• natural 🧑 or unnatural 🤖

Depending on your training objectives you will want lots of low quality instruction data, or a small amount of high quality data. Which should you use? Lets see what Anthropic have to say in Askell et al:

How can we improve the sample efficiency of preference modeling? We find that we can significantly improve sample efficiency using a ‘preference model pre-training’ (PMP) stage of training, where we first pre-train on large public datasets that encode human preference information, such as Stack Exchange, Reddit, and Wikipedia edits, before finetuning on smaller datasets encoding more specific human preferences.

Natural 🧑 & High quality 🗹

• OASST1- OpenAssistant Conversations Dataset 160k rows, 2023-04-12
• SHP - Stanford human preferences - a dataset of instructions inferred from high quality sbureddits. 300k rows. 2023-02-23 tweet
• HH-RLHF - Antropic RLHF 91k rows
• allenai/natural-instructions 64k rows
• hendrycks/ethics 130k rows

Natural 🧑 & Lower quality ☐

• ELI5: a reddit based dataset of questions and answers. The SHP dataset improved on it's processing by comparing score and time
• https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences 10M instruction stack exchange, was used in anthropic paper paper]:

Unnatural 🤖 & High quality 🗹

• alpaca_data_cleaned.json GPT4 instruction data, with heavy curation
• https://github.com/teknium1/GPTeacher
• https://github.com/databrickslabs/dolly
• OIG-small-chip2 a subset of the OIG dataset
• shareGPT 90k

Unnatural 🤖 & Lower quality ☐

• unnatural-instructions used above and GPT3 to make 256k examples
• OIG - Open Instruction Generalist Dataset a compilation of ~43M instructions. "The OIG dataset is almost purely a synthetic data set created using data augmentation.""
  • note there is a higher quality subset OIG-small-chip2

Uncategorized

• b-mc2 (https://huggingface.co/datasets/b-mc2/wikihow_lists).

Finding more data

A great way to find new instruction datasets is to

• [search huggingface's datasets](all hf data 1)
• Look at compilations like - OIG
• github instruction-turning tag

Training

Libraries

• https://github.com/lucidrains/PaLM-rlhf-pytorch - Implementation of RLHF on top of the PaLM

• https://github.com/CarperAI/trlx - A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)

• https://huggingface.co/docs/trl/index transformer reinforcement learning

• https://github.com/allenai/RL4LMs

• https://github.com/voidful/TextRL

tutorials

• https://huggingface.co/blog/stackllama - StackLLaMA: A hands-on guide to train LLaMA with RLHF
• https://huggingface.co/blog/rlhf

Papers/Methods

• RLHF
• Chain of Hindsight https://arxiv.org/abs/2302.02676 the model it trained to rank it's own output, so it's kind of like diffusion, letting the model operate iterativly.
• SFT - Supervised Fine Tuning this is normal fine tuning
• Pretraining Language Models with Human Preferences tweet You can (and should) do RL from human feedback during pretraining itself! In our new paper, we show how training w/ human preferences early on greatly reduces undesirable LM behaviors
• HIR: Hindsight Instruction Relabeling 💩 offline RL reinvented with extra steps FARL: SL Algorithm Distillation: classical control problems. Offline RL
• Hindsight Instruction Relabeling (HIR), https://arxiv.org/abs/2302.05206 " outperforms the baseline algorithms and is comparable to or even surpasses supervised finetuning. "

Evaluation

There are multiple ways to formally evaluate LLM capabilities. Right now project generally use one of these 3 libraries. Personally I prefer Eleuther's work, but opinions and github stars are divided.

• python api:
  • huggingface/evaluate this is not specific to LLM's or RLHF, but some projects find it and easy to use starting point.
• cli api:
  • EleutherAI/lm-evaluation-harness - has lots of datasets like GLUE and ETHICS already included, works with huggingface
  • openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks. - has lots of rare eval sets like sarcasm, works with langchain
  • stanford-crfm/helm: Holistic Evaluation of Language Models (HELM), a framework to increase the transparency of language models (https://arxiv.org/abs/2211.09110)., works with huggingface

Similar lists

• very comprehensive list https://github.com/yaodongC/awesome-instruction-dataset ⭐
• divides the data in a similar way https://github.com/raunak-agarwal/instruction-datasets
• has tables https://github.com/zhilizju/Awesome-instruction-tuning
• papers https://github.com/SinclairCoder/Instruction-Tuning-Papers