# DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs

arXiv: https://arxiv.org/abs/2506.08500 · HTML: https://mlsanity.com/paper/2506.08500/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 45 of its 53 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 30 of 45)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Entity-Based Knowledge Conflicts in Question Answering](https://mlsanity.com/paper/2109.05052/) (2021)
- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://mlsanity.com/paper/2104.06967/) (2021)
- [ASQA: Factoid Questions Meet Long-Form Answers](https://mlsanity.com/paper/2204.06092/) (2022)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [VERISCORE: Evaluating the factuality of verifiable claims in long-form text generation](https://mlsanity.com/paper/2406.19276/) (2024)
- [A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing](https://mlsanity.com/paper/2310.08433/) (2023)
- [Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs](https://mlsanity.com/paper/2311.09469/) (2023)
- [Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://mlsanity.com/paper/2405.16908/) (2024)
- [FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"](https://mlsanity.com/paper/2410.03727/) (2024)

## Built on by (most cited first, 25 of 25)

- [Deep Research: A Survey of Autonomous Research Agents](https://mlsanity.com/paper/2508.12752/) (2025)
- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [A Unified Definition of Hallucination: It's The World Model, Stupid!](https://mlsanity.com/paper/2512.21577/) (2025)
- [Consensus or Conflict? Fine-Grained Evaluation of Conflicting Answers in Question-Answering](https://mlsanity.com/paper/2508.12355/) (2025)
- [EvidenceRL: Reinforcing Evidence Consistency for Trustworthy Language Models](https://mlsanity.com/paper/2603.19532/) (2026)
- [ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation](https://mlsanity.com/paper/2605.17301/) (2026)
- [Addressing Corpus Knowledge Poisoning Attacks on RAG Using Sparse Attention](https://mlsanity.com/paper/2602.04711/) (2026)
- [To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG](https://mlsanity.com/paper/2606.25191/) (2026)
- [When Abundance Conceals Weakness: Knowledge Conflict in Multilingual Models](https://mlsanity.com/paper/2601.07041/) (2026)
- [CryptoAnalystBench: Failures in Multi-Tool Long-Form LLM Analysis](https://mlsanity.com/paper/2602.11304/) (2026)
- [Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions](https://mlsanity.com/paper/2604.12138/) (2026)
- [Enhancing LLM Metacognition via Cognitive Pairwise Training](https://mlsanity.com/paper/2606.00869/) (2026)
- [When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict](https://mlsanity.com/paper/2608.13921/) (2026)
- [Towards Evolving Context Parameterization for Large Language Models](https://mlsanity.com/paper/2609.14168/) (2026)
- [On Finding Inconsistencies in Documents](https://mlsanity.com/paper/2512.18601/) (2025)
- [DETOUR: An Interactive Benchmark for Dual-Agent Search and Reasoning](https://mlsanity.com/paper/2602.00352/) (2026)
- [DeepStress: Stress-Testing Deep Search Agents](https://mlsanity.com/paper/2607.13920/) (2026)
- [Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs](https://mlsanity.com/paper/2607.25959/) (2026)
- [Large Language Models in Resolving Contextual Knowledge Conflicts](https://mlsanity.com/paper/2609.03148/) (2026)
- [Stochastic Semantic Evidence Graphs: Uncertainty Propagation and Governance for Agentic AI](https://mlsanity.com/paper/2609.29703/) (2026)
- [Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities](https://mlsanity.com/paper/2610.00606/) (2026)
- [InvestigationWorlds: An Agentic Environment for Legal Investigation](https://mlsanity.com/paper/2610.04129/) (2026)
- [Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents](https://mlsanity.com/paper/2610.17827/) (2026)
- [Which Knowledge Should LLMs Trust? Uncovering a Generalizable Steering Direction Across Knowledge Conflicts](https://mlsanity.com/paper/2610.20067/) (2026)

## Abstract (arXiv)

Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing large language models (LLMs) with relevant and up-to-date information. However, the retrieved sources can often contain conflicting information and it remains unclear how models should address such discrepancies. In this work, we first propose a novel taxonomy of knowledge conflict types in RAG, along with the desired model behavior for each type. We then introduce CONFLICTS, a high-quality benchmark with expert annotations of conflict types in a realistic RAG setting. CONFLICTS is the first benchmark that enables tracking progress on how models address a wide range of knowledge conflicts. We conduct extensive experiments on this benchmark, showing that LLMs often struggle to appropriately resolve conflicts between sources. While prompting LLMs to explicitly reason about the potential conflict in the retrieved documents significantly improves the quality and appropriateness of their responses, substantial room for improvement in future research remains.