Summary of Certifiably Robust Rag Against Retrieval Corruption, by Chong Xiang et al.
Certifiably Robust RAG against Retrieval Corruption
by Chong Xiang, Tong Wu, Zexuan Zhong, David Wagner, Danqi Chen, Prateek Mittal
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, RobustRAG, is designed to defend against retrieval corruption attacks in retrieval-augmented generation (RAG) models. The key strategy involves isolating responses from each passage before aggregating them securely. To achieve this, the authors develop keyword-based and decoding-based algorithms for unstructured text responses. Notably, RobustRAG can provide certifiable robustness for certain queries, ensuring accurate results even when an attacker injects malicious passages. The framework is evaluated on open-domain QA and long-form text generation datasets, demonstrating its effectiveness and generalizability across various tasks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RobustRAG is a new way to keep RAG models safe from hackers who can make them say the wrong things. This happens when attackers add fake information into the model’s answers. RobustRAG makes sure that even if an attacker adds some bad information, the model will still give the right answer. It does this by getting the model’s responses for each piece of text separately and then combining them in a special way. The authors tested RobustRAG on two types of tasks: answering questions and generating long texts. They showed that it works well and can be used with different models and datasets. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation » Text generation
