A Framework of Reflective Agents with Adaptive Collaboration for Attributed Summary Generation

Abstract

Despite progress in LLM summarization, factual hallucinations persist, motivating Attributed Summary Generation (ASG), which requires sentence-level citations. However, existing prompt-based approaches face severe challenges such as positional preference, poor citation quality and sensitivity to uninformative documents. In view of these limitations, we propose \mathbf{RAAC}, a framework of \mathbf{R}eflective \mathbf{A}gents with \mathbf{A}daptive \mathbf{C}ollaboration for attributed summarization. RAAC performs iterative summarization via reflective agents’ collaboration, where a post reflection module evaluates the consistency between the summary and the input documents, based on which it critiques the summary and uses the resulting feedback to recalibrate the inputs to the next adaptive iteration. The agents’ collaboration involves two components: \mathsf{TextAgent} and \mathsf{CitationAgent}. Experimental results on the ALCE benchmark demonstrate that our framework outperforms existing baselines in both factual correctness and citation quality.