Papers by Yingtao Ren

1 papers
Context-attended Adversarial Reinforcement Learning for Robust Multi-step Retrieval Augmented Generation (2026.findings-acl)

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Challenge: Existing approaches to multi-step retrieval-augmented generation are susceptible to retrieval noise and fabricated documents in real-world scenarios.
Approach: They propose a framework for multi-step retrieval-augmented generation that incorporates external knowledge into a retriever to generate responses from adversarial samples.
Outcome: The proposed framework improves performance in multiple noisy scenarios and can be used to improve multi-step retrieval-augmented generation.

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