Atomic Fact Decomposition Helps Attributed Question Answering

TL;DR

This paper introduces an Atomic Fact Decomposition framework for Attributed Question Answering that improves answer accuracy and attribution by decomposing answers into atomic facts, retrieving relevant evidence, and editing answers accordingly.

Contribution

The paper proposes a novel Atomic fact decomposition-based retrieval and editing framework, leveraging instruction-tuned LLMs and knowledge graphs for improved AQA performance.

Findings

Outperforms state-of-the-art methods on multiple datasets.

Introduces a new metric $Attr_{p}$ for evidence attribution precision.

Demonstrates effectiveness of atomic fact decomposition in complex answer editing.

Abstract

Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, including two general paradigms: Retrieval-Then-Read (RTR) and post-hoc retrieval. Recently, Large Language Models (LLMs) have shown remarkable proficiency, prompting growing interest in AQA among researchers. However, RTR-based AQA often suffers from irrelevant knowledge and rapidly changing information, even when LLMs are adopted, while post-hoc retrieval-based AQA struggles with comprehending long-form answers with complex logic, and precisely identifying the content needing revision and preserving the original intent. To tackle these problems, this paper proposes an Atomic fact decomposition-based Retrieval and Editing (ARE) framework, which decomposes the generated long-form answers into molecular clauses and atomic facts by the instruction-tuned LLMs. Notably, the instruction-tuned LLMs are fine-tuned using a well-constructed dataset, generated from large scale Knowledge Graphs (KGs). This process involves extracting one-hop neighbors from a given set of entities and transforming the result into coherent long-form text. Subsequently, ARE leverages a search engine to retrieve evidences related to atomic facts, inputting these evidences into an LLM-based verifier to determine whether the facts require expansion for re-retrieval or editing. Furthermore, the edited facts are backtracked into the original answer, with evidence aggregated based on the relationship between molecular clauses and atomic facts. Extensive evaluations demonstrate the superior performance of our proposed method over the state-of-the-arts on several datasets, with an additionally proposed new metric $Attr_{p}$ for evaluating the precision of evidence attribution.