# Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs

arXiv: https://arxiv.org/abs/2402.10612 · HTML: https://mlsanity.com/paper/2402.10612/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Placed in

- Retrieval-augmented generation / [Hallucination detection in RAG](https://mlsanity.com/rag/hallucination-detection-in-rag/)

## Builds on (most cited first, 21 of 21)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://mlsanity.com/paper/2101.02235/) (2021)
- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation](https://mlsanity.com/paper/2307.03987/) (2023)
- [Large Language Models with Controllable Working Memory](https://mlsanity.com/paper/2211.05110/) (2022)
- [LM vs LM: Detecting Factual Errors via Cross Examination](https://mlsanity.com/paper/2305.13281/) (2023)
- [Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation](https://mlsanity.com/paper/2307.11019/) (2023)
- [Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework](https://mlsanity.com/paper/2305.03268/) (2023)
- [Do Language Models Know When They're Hallucinating References?](https://mlsanity.com/paper/2305.18248/) (2023)
- [SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency](https://mlsanity.com/paper/2311.01740/) (2023)
- [Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction](https://mlsanity.com/paper/2306.03378/) (2023)

## Built on by (most cited first, 27 of 27)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely](https://mlsanity.com/paper/2409.14924/) (2024)
- [RATT: A Thought Structure for Coherent and Correct LLM Reasoning](https://mlsanity.com/paper/2406.02746/) (2024)
- [ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2411.15268/) (2024)
- [UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities](https://mlsanity.com/paper/2504.20734/) (2025)
- [Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards](https://mlsanity.com/paper/2603.09117/) (2026)
- [CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control](https://mlsanity.com/paper/2405.18727/) (2024)
- [MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models](https://mlsanity.com/paper/2404.04990/) (2024)
- [A Theory for Token-Level Harmonization in Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.00944/) (2024)
- [Adaptive Stopping for Multi-Turn LLM Reasoning](https://mlsanity.com/paper/2604.01413/) (2026)
- [To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling](https://mlsanity.com/paper/2605.00737/) (2026)
- [Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation](https://mlsanity.com/paper/2505.21072/) (2025)
- [Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights](https://mlsanity.com/paper/2511.06738/) (2025)
- [Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Models](https://mlsanity.com/paper/2506.07575/) (2025)
- [Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.18003/) (2024)
- [Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE](https://mlsanity.com/paper/2509.03626/) (2025)
- [How You Ask Matters! Adaptive RAG Robustness to Query Variations](https://mlsanity.com/paper/2604.10745/) (2026)
- [Retrieval-augmented Decoding for Improving Truthfulness in Open-ended Generation](https://mlsanity.com/paper/2508.02184/) (2025)
- [Fast, Slow, and Tool-augmented Thinking for LLMs: A Review](https://mlsanity.com/paper/2508.12265/) (2025)
- [Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability](https://mlsanity.com/paper/2412.15101/) (2024)
- [Addressing the sustainable AI trilemma: a case study on LLM agents and RAG](https://mlsanity.com/paper/2501.08262/) (2025)
- [GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs](https://mlsanity.com/paper/2505.10143/) (2025)
- [Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought](https://mlsanity.com/paper/2509.03918/) (2025)
- [From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents](https://mlsanity.com/paper/2608.16002/) (2026)
- [Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning](https://mlsanity.com/paper/2608.28018/) (2026)
- [Return or Revise? Learning When Revision Helps Retrieval-Augmented QA](https://mlsanity.com/paper/2609.30087/) (2026)
- [Iterative Tree Analysis for Medical Critics](https://mlsanity.com/paper/2501.10642/) (2025)

## Abstract (arXiv)

Hallucinations present a significant challenge for large language models (LLMs). The utilization of parametric knowledge in generating factual content is constrained by the limited knowledge of LLMs, potentially resulting in internal hallucinations. While incorporating external information can help fill knowledge gaps, it also introduces the risk of irrelevant information, thereby increasing the likelihood of external hallucinations. To balance the use of parametric knowledge within LLMs and external information, in this study, we present Rowen, a novel framework that enhances LLMs with an adaptive retrieval augmentation process tailored to address hallucinated outputs. Rowen introduces a consistency-based hallucination detection module, which assesses the model's uncertainty regarding the input query by evaluating the semantic inconsistencies in various responses generated across different languages or models. When high uncertainties in the responses are detected, Rowen activates the retrieval of external information to rectify the model outputs. Through comprehensive empirical experiments, we demonstrate that Rowen surpasses the current state-of-the-art in both detecting and mitigating hallucinated content within the outputs of LLMs.