Google Research

Attributed QA


Large language models (LLMs) have shown impressive results across a variety of tasks while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial for both system developers and users in this setting. We propose and study Attributed QA as a key first step in the development of attributed LLMs.

This release consists of human-rated system outputs for a new question-answering task, Attributed Question Answering (AQA). In AQA, the input is a question, and the output is an (answer, attribution) pair where answer is an answer string, and attribution is a pointer into a fixed underlying corpus, in our case, Wikipedia. In releasing this, we hope to foster research into an important research question, How to build LLMs with attribution?