Languages are Rewards: Hindsight Finetuning using Human Feedback

Learning from human preferences is important for language models to be helpful and useful for humans, and to align with human and social values. Existing works focus on supervised finetuning of pretrained models, based on curated model generations that are preferred by human labelers. Such works have achieved remarkable successes in understanding and following instructions (e.g., InstructGPT, ChatGPT, etc). However, to date, a key limitation of supervised finetuning is that it cannot learn from negative ratings; models are only trained on positive-rated data, which makes it data inefficient. Because collecting human feedback data is both time consuming and expensive, it is vital for the model to learn from all feedback, akin to the remarkable ability of humans to learn from diverse feedback. In this work, we propose a novel technique called Hindsight Finetuning for making language models learn from diverse human feedback. In fact, our idea is motivated by how humans learn from hindsight experience. We condition the model on a sequence of model generations paired with hindsight feedback, and finetune the model to predict the most preferred output. By doing so, models can learn to identify and correct negative attributes or errors. Applying the method to GPT-J, we observe that it significantly improves results on summarization and dialogue tasks using the same amount of human feedback.