Abstract
The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development. We build upon this observation by formalizing an algorithm for learning from natural language feedback at training time instead, which we call Imitation learning from Language Feedback (ILF). ILF requires only a small amount of human-written feedback during training and does not require the same feedback at test time, making it both user-friendly and sample-efficient. We further show that ILF can be seen as a form of minimizing the KL divergence to the ground truth distribution and demonstrate a proof-of-concept on a neural program synthesis task. We use ILF to improve a Codegen-Mono 6.1B model’s pass@1 rate by 38% relative (and 10% absolute) on the Mostly Basic Python Problems (MBPP) benchmark, outperforming both fine-tuning on MBPP and fine-tuning on repaired programs written by humans. Overall, our results suggest that learning from human-written natural language feedback is both more effective and sample-efficient than training exclusively on demonstrations for improving an LLM’s performance on code generation tasks.
Jérémy Scheurer
Research Scientist
Jérémy graduated with an MS in Computer Science from ETH Zurich, and is currently a visiting researcher at New York University. He used to work at FAR AI with Ethan Perez, aligning language models to human preferences.
Tomasz Korbak
PhD Student
Tomas is a PhD student at the Department of Informatics, University of Sussex working on deep reinforcement learning and generative models with Chris Buckley and Anil Seth. He focuses on probabilistic approaches to control, such as active inference and control-as-inference, and controllable generative modelling. Tomas previously worked at FAR with Ethan Perez and Sam Bowman on aligning language models with human preferences. For more information, see his website.
Ethan Perez
Research Scientist
Ethan is a Research Scientist at Anthropic. He completed his Ph.D. in Natural Language Processing at New York University. He was advised by Kyunghyun Cho and Douwe Kiela and funded by NSF and Open Philanthropy. His research focuses on aligning language models with human preferences, e.g., for content that is helpful, honest, and harmless. In particular, he is excited about developing learning algorithms that outdo humans at generating such content, by producing text that is free of social biases, cognitive biases, common misconceptions, and other limitations. Previously, he has spent time at DeepMind, Facebook AI Research, Montreal Institute for Learning Algorithms, Uber, and Google. He earned a Bachelor’s from Rice University as the Engineering department’s Outstanding Senior. Visit his website to find out more.