Multi-agent retrieval method reduces unsupported claims in language models
Hallucination-Aware Multi-Agent RAG via Internal Attention Rewards
Information Retrieval
Summary
Language models sometimes make up facts not supported by their sources when answering questions. The authors introduce a system where multiple agents work together and get feedback not only on getting the right answer, but also on whether the answer sticks to the evidence. They use a special attention-based signal and a trained critic to reward truthful answers. Their method improves accuracy and reduces made-up content across several language model types and question benchmarks.
What this means in practice
- •For conversational ai developers: Improve chatbot responses by reducing unsupported claims through multi-agent coordinated retrieval and generation with hallucination-aware rewards.
- •For digital assistants teams: Enhance virtual assistant accuracy by integrating attention-based hallucination risk signals during answer generation.
Authors
Runsong Jia, Qian Chang, Mengjia Wu, Ciprian Doru Giurcăneanu, Jie Lu, Yi Zhang
Abstract
Retrieval-augmented generation (RAG) grounds language models in retrieved evidence, but its outputs often contain claims unsupported by that evidence. Recent multi-agent RAG systems decompose the pipeline into cooperative decisions, yet they typically optimize all agents under a shared task reward such as EM or F1, which measures answer overlap rather than faithfulness to evidence. We propose a hallucination-aware multi-agent RAG framework that combines the task reward with an internal hallucination-risk signal computed from the generator's attention-head norms. Three role-specific LoRA adapters, a query planner, an evidence selector, and an answer generator, share a frozen backbone. A lightweight critic is trained once on TruthfulQA truthful/false pairs and then kept frozen, providing a complementary risk score that is added to the shared reward during cooperative PPO training. Across three backbones (Qwen3-8B, Llama3-8B, Llama2-13B) and three benchmarks (RAGTruth, HotpotQA, 2WikiMultiHopQA), our method improves task performance and reduces hallucination rates over the strongest multi-agent baseline. Ablations and critic diagnostics show that the TruthfulQA-calibrated signal provides useful reward shaping that transfers across RAG tasks.