RAGStress: A controlled benchmark for evaluating retrieval-augmented generation under knowledge-base degradation
cs.AI
Submitted: 2026-10-03
Updated: 2026-10-03
Code: https://github.com/Salliberg/CorruptRAG-Code
Terminology
Sources
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Retrieval-Augmented Generation for Large Language Models: A Survey
- Measuring Massive Multitask Language Understanding
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
- PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
- Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation
- BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models
- Poisoning Retrieval Corpora by Injecting Adversarial Passages
- Practical Poisoning Attacks against Retrieval-Augmented Generation
- Benchmarking Poisoning Attacks against Retrieval-Augmented Generation
- Certifiably Robust RAG against Retrieval Corruption
- Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
- Knowledge Conflicts for LLMs: A Survey
- Entity-Based Knowledge Conflicts in Question Answering
- Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
- DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
- Large Language Models Can Be Easily Distracted by Irrelevant Context
- Context-Parametric Inversion: Why Instruction Finetuning Can Worsen Context Reliance
- ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence
- Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals
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