Advancing Large Language Model Attribution through Self-Improving

TL;DR

This paper introduces START, a self-improving framework that enhances large language models' ability to generate verifiable, citation-based responses without manual data annotation, significantly improving attribution accuracy.

Contribution

The paper presents a novel self-taught attribution framework that iteratively improves LLMs' citation capabilities using synthetic data and preference supervision, reducing reliance on manual annotations.

Findings

Achieved 25.13% performance improvement on open-domain QA datasets.

Demonstrated effectiveness across long-form QA and multi-step reasoning tasks.

Outperformed models relying on human-annotated data without additional supervision.

Abstract

Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems. However, improving this capability requires high-quality attribution data, which is costly and labor-intensive. Inspired by recent advances in self-improvement that enhance LLMs without manual annotation, we present START, a Self-Taught AttRibuTion framework for iteratively improving the attribution capability of LLMs. First, to prevent models from stagnating due to initially insufficient supervision signals, START leverages the model to self-construct synthetic training data for warming up. To further self-improve the model's attribution ability, START iteratively utilizes fine-grained preference supervision signals constructed from its sampled responses to encourage robust, comprehensive, and attributable generation. Experiments on three open-domain question-answering datasets, covering long-form QA and multi-step reasoning, demonstrate significant performance gains of 25.13% on average without relying on human annotations and more advanced models. Further analysis reveals that START excels in aggregating information across multiple sources.