Qing Li, Chen, Jason Zhang, Wentao Hu, Yiyang Jiang, Wengyu Zhang, ♠ Xiaoyong, Melissa Gu Xixi, Laurie Forman
We lifted 4 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| huenao/debate-augmented-rag | canonical | 1 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| single_agent_pred_parse | Ran | huenao/debate-augmented-rag/model/utils.py code served (permissive licence) · get_code("1427130e07bc28d6") |
| flare | Not yet run | huenao/debate-augmented-rag/model/baseline.py code served (permissive licence) · get_code("01dc9bbd9d614506") |
| naive_gen | Not yet run | huenao/debate-augmented-rag/model/baseline.py code served (permissive licence) · get_code("b2aebffab76a7b25") |
| naive_rag | Not yet run | huenao/debate-augmented-rag/model/baseline.py code served (permissive licence) · get_code("b52981e3c87a6c8d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Retrieval-Augmented Generation (RAG) enhances factual accuracy by integrating external knowledge, yet it introduces a critical issue: erroneous or biased retrieval can mislead generation, compounding hallucinations, a phenomenon we term Hallucination on Hallucination. To address this, we propose Debate-Augmented RAG (DRAG), a training-free framework that integrates Multi-Agent Debate (MAD) mechanisms into both retrieval and generation stages. In retrieval, DRAG employs structured debates among proponents, opponents, and judges to refine retrieval quality and ensure factual reliability. In generation, DRAG introduces asymmetric information roles and adversarial debates, enhancing reasoning robustness and mitigating factual inconsistencies. Evaluations across multiple tasks demonstrate that DRAG improves retrieval reliability, reduces RAG-induced hallucinations, and significantly enhances overall factual accuracy. Our code is available at https://github.com/ Huenao/Debate-Augmented-RAG.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2505.18581")
get_code_for_paper("2505.18581")
have("2505.18581")
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