WebGPT: Browser-assisted question-answering with human feedback

Summary

This paper introduces WebGPT, a question-answering system designed to answer questions using the internet, specifically with browser assistance. It leverages a reinforcement learning approach with human feedback to improve the quality, reliability, and factuality of the responses. WebGPT utilizes a large language model that is trained to browse the internet, retrieve relevant documents, and synthesize information to answer user questions. The model is iteratively refined through human feedback, where human annotators rate the quality of the model's responses, leading to improved performance. The paper evaluates WebGPT's ability to generate answers that are more accurate, supported by evidence from the web, and less likely to contain fabricated information compared to baseline language models. The core of WebGPT lies in its approach to integrating web browsing and human feedback within a reinforcement learning framework, showcasing a significant step towards building question-answering systems capable of accessing and utilizing information from the open web.

Key Takeaways

1. WebGPT demonstrates a novel approach to building question-answering systems by integrating web browsing capabilities into a large language model.
2. Human feedback is a crucial component in training WebGPT, significantly improving the accuracy and reliability of the answers generated.
3. The research highlights the effectiveness of using reinforcement learning with human feedback to guide language models in complex tasks like information retrieval and answer synthesis.
4. WebGPT provides a benchmark for future research in question-answering and the integration of web access with large language models.