# FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2506.08938 · HTML: https://mlsanity.com/paper/2506.08938/

## Key facts

- Cited by 28 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 27 of its 39 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, 27 of 27)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Entity-Based Knowledge Conflicts in Question Answering](https://mlsanity.com/paper/2109.05052/) (2021)
- [Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts](https://mlsanity.com/paper/2305.13300/) (2023)
- [Trusting Your Evidence: Hallucinate Less with Context-aware Decoding](https://mlsanity.com/paper/2305.14739/) (2023)
- [Generate rather than Retrieve: Large Language Models are Strong Context Generators](https://mlsanity.com/paper/2209.10063/) (2022)
- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models](https://mlsanity.com/paper/2410.02355/) (2024)
- [Large Language Models with Controllable Working Memory](https://mlsanity.com/paper/2211.05110/) (2022)
- [Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization](https://mlsanity.com/paper/2406.16008/) (2024)
- [ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://mlsanity.com/paper/2401.10225/) (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)
- [AnyEdit: Edit Any Knowledge Encoded in Language Models](https://mlsanity.com/paper/2502.05628/) (2025)
- [ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities](https://mlsanity.com/paper/2407.14482/) (2024)
- [Context-DPO: Aligning Language Models for Context-Faithfulness](https://mlsanity.com/paper/2412.15280/) (2024)
- [Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?](https://mlsanity.com/paper/2401.11911/) (2024)
- [Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM](https://mlsanity.com/paper/2402.11517/) (2024)
- [Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint](https://mlsanity.com/paper/2402.11893/) (2024)
- [Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs](https://mlsanity.com/paper/2406.02376/) (2024)
- [Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts](https://mlsanity.com/paper/2309.17415/) (2023)
- [SFR-RAG: Towards Contextually Faithful LLMs](https://mlsanity.com/paper/2409.09916/) (2024)
- [Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness](https://mlsanity.com/paper/2404.00216/) (2024)
- [Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation](https://mlsanity.com/paper/2502.12911/) (2025)

## Built on by (most cited first, 28 of 28)

- [LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora](https://mlsanity.com/paper/2510.10114/) (2025)
- [You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures](https://mlsanity.com/paper/2508.06105/) (2025)
- [TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs](https://mlsanity.com/paper/2511.10375/) (2025)
- [MemSyco-Bench: Benchmarking Sycophancy in Agent Memory](https://mlsanity.com/paper/2607.01071/) (2026)
- [From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG](https://mlsanity.com/paper/2603.03292/) (2026)
- [A Unified Definition of Hallucination: It's The World Model, Stupid!](https://mlsanity.com/paper/2512.21577/) (2025)
- [ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation](https://mlsanity.com/paper/2605.17301/) (2026)
- [LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval](https://mlsanity.com/paper/2508.07690/) (2025)
- [HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Stores](https://mlsanity.com/paper/2509.21336/) (2025)
- [ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents](https://mlsanity.com/paper/2606.18037/) (2026)
- [Copy-Paste to Mitigate Large Language Model Hallucinations](https://mlsanity.com/paper/2510.00508/) (2025)
- [CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering](https://mlsanity.com/paper/2602.23952/) (2026)
- [Diagnosing Knowledge Conflict in Multimodal Long-Chain Reasoning](https://mlsanity.com/paper/2602.14518/) (2026)
- [Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions](https://mlsanity.com/paper/2604.12138/) (2026)
- [Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models](https://mlsanity.com/paper/2604.25313/) (2026)
- [Trust or Abstain? A Self-Aware RAG Approach](https://mlsanity.com/paper/2605.18792/) (2026)
- [MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.00610/) (2026)
- [Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models](https://mlsanity.com/paper/2606.04109/) (2026)
- [Document-Authored Control-Signal Impersonation: A Low-Cost Indirect Prompt Attack on RAG Safety Boundaries](https://mlsanity.com/paper/2606.09005/) (2026)
- [Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference](https://mlsanity.com/paper/2606.20245/) (2026)
- [Can LLMs Take Retrieved Information with a Grain of Salt?](https://mlsanity.com/paper/2605.06919/) (2026)
- [X-MADAM-RAG: Diagnosing and Handling Chinese-English Evidence Conflict in Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.12903/) (2026)
- [When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.16515/) (2026)
- [MC-CXR: A Multi-Context Chest X-ray Benchmark for Context-Induced Disruption in Vision-Language Models](https://mlsanity.com/paper/2608.24118/) (2026)
- [From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations](https://mlsanity.com/paper/2608.25894/) (2026)
- [Knowing Before Answering: Decoding Language Models for Reliable RAG](https://mlsanity.com/paper/2608.27661/) (2026)
- [Regime Boundary Alignment for Evidence-Gated Question Answering](https://mlsanity.com/paper/2609.37491/) (2026)
- [Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancy](https://mlsanity.com/paper/2610.05162/) (2026)

## Abstract (arXiv)

Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM`s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model`s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model`s parametric knowledge, which undermines the model`s internal knowledge structure and increases the risk of misinterpreting the context. To this end, this paper proposes FaithfulRAG, a novel framework that resolves knowledge conflicts by explicitly modeling discrepancies between the model`s parametric knowledge and retrieved context. Specifically, FaithfulRAG identifies conflicting knowledge at the fact level and designs a self-thinking process, allowing LLMs to reason about and integrate conflicting facts before generating responses. Extensive experiments demonstrate that our method outperforms state-of-the-art methods. The code is available at https://github.com/DeepLearnXMU/Faithful-RAG