RAGnRoll: Learning to Iteratively Retrieve and Generate Attributable Answer Snippets
TL;DR
This work proposes RAGnRoll, a language model for attributed answer generation within a multi-round Retrieval-Augmented Generation (RAG) framework that leverages the iterative nature of multi-round RAG to train an LLM to incrementally build answers guided by subqueries.
Abstract
The rapid adoption of generative search engines has marked a significant shift in information retrieval. New approaches leverage Large Language Models (LLMs) to provide synthesized, contextually rich responses in natural language to directly satisfy users’ information needs. Due to their generative nature, these systems benefit from enhanced reliability by integrating explicit references to supporting sources in their outputs, otherwise known as Attribution. Teaching LLMs this capability is a challenging task that heavily depends on training data quality and strong training signals, especially for scenarios that require digesting long contexts and providing comprehensive answers. Planning and decomposition have notoriously helped language models produce better answers, but their impact on attribution remains relatively unexplored. This work proposes RAGnRoll, a language model for attributed answer generation within a multi-round Retrieval-Augmented Generation (RAG) framework. We leverage the iterative nature of multi-round RAG to train an LLM to incrementally build answers guided by subqueries. RAGnRoll is trained using a causal language modeling objective and a small, specially crafted dataset. Empirical evaluations on four different datasets for attributed long answer generation (ASQA, ELI5) and information-seeking (HAGRID and CORAL) demonstrate RAGnRoll's impact on both answer correctness and attribution accuracy. We extensively evaluate RAGnRoll training approach against baseline methods and benchmark its performance at inference against strong state-of-the-art models. RAGnRoll achieves competitive performance with advanced approaches without relying on extensive data or complex training.