Training language models to follow instructions with human feedback
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning wi
The Full Story of Large Language Models and RLHF
Large Language Models have been in the limelight since the release of ChatGPT, with new models being announced seemingly every week. This guide walks through the essential ideas of how these models came to be. In this article we give a comprehensive overview of what’s really going on in the world of Language Models, building from the foundational ideas, all the way to the latest advancements. What
GitHub - opendilab/awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated)
(The following section was automatically generated by ChatGPT) RLHF typically refers to "Reinforcement Learning with Human Feedback". Reinforcement Learning (RL) is a type of machine learning that involves training an agent to make decisions based on feedback from its environment. In RLHF, the agent also receives feedback from humans in the form of ratings or evaluations of its actions, which can
ChatGPTを支えた高品質AI作成手法「RLHF」の中身はこんな感じ、面倒なデータ入力・整理はオープンソースでセルフホスト可能なプラットフォーム「Argilla」が便利
RLHFとは「人間の評価による強化学習」のことで、大規模言語モデルをChatGPTなどの実用レベルに至る品質にまで高めた実績のある手法です。RLHFでは教師データを作成したり、大規模言語モデルの回答を評価したりする際に人間がデータを入力する必要があり、特に複数人で作業する場合にデータの管理が大変になってしまうものですが、そうしたRLHF用データの入力や管理を行ってくれるプラットフォームが「Argilla」です。 Bringing LLM Fine-Tuning and RLHF to Everyone https://argilla.io/blog/argilla-for-llms/ 大規模言語モデルを作成する時の手順を示したのが下の図です。まず大量のテキストを用いて事前学習を行います。こうして作成されたモデルが事前学習済みモデルで、GPTやPaLM、LLaMAなどのモデルがこのカテゴリに
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