# HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2503.04800 · HTML: https://mlsanity.com/paper/2503.04800/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 27 of 27)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://mlsanity.com/paper/2310.03214/) (2023)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models](https://mlsanity.com/paper/2205.11388/) (2022)
- [TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models](https://mlsanity.com/paper/2204.14211/) (2022)
- [A Critical Evaluation of Evaluations for Long-form Question Answering](https://mlsanity.com/paper/2305.18201/) (2023)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Models](https://mlsanity.com/paper/2311.08106/) (2023)
- [PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering](https://mlsanity.com/paper/2402.11034/) (2024)
- [GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?](https://mlsanity.com/paper/2406.05606/) (2024)
- [Language Modeling with Editable External Knowledge](https://mlsanity.com/paper/2406.11830/) (2024)
- [Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering](https://mlsanity.com/paper/2409.16909/) (2024)
- [Revisiting the Solution of Meta KDD Cup 2024: CRAG](https://mlsanity.com/paper/2409.15337/) (2024)

## Built on by (most cited first, 13 of 13)

- [DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis](https://mlsanity.com/paper/2508.20033/) (2025)
- [PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature](https://mlsanity.com/paper/2510.10909/) (2025)
- [RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge](https://mlsanity.com/paper/2510.13590/) (2025)
- [Memory in Large Language Models: Mechanisms, Evaluation and Evolution](https://mlsanity.com/paper/2509.18868/) (2025)
- [Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation](https://mlsanity.com/paper/2507.07307/) (2025)
- [Knowledge-Grounded Agentic Large Language Models for Multi-Hazard Understanding from Reconnaissance Reports](https://mlsanity.com/paper/2511.14010/) (2025)
- [Mind2Report: Expert-Level Commercial Report Synthesis via Cognitive Deep Research Agent](https://mlsanity.com/paper/2601.04879/) (2026)
- [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026)
- [Agents Trust Tools Too Much: Measuring Reliance on Unreliable Tools](https://mlsanity.com/paper/2609.05587/) (2026)
- [Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval](https://mlsanity.com/paper/2509.01306/) (2025)
- [ChronoPlay: A Framework for Modeling Dual Dynamics and Authenticity in Game RAG Benchmarks](https://mlsanity.com/paper/2510.18455/) (2025)
- [Can Structural Cues Save LLMs? Evaluating Language Models in Massive Document Streams](https://mlsanity.com/paper/2603.19250/) (2026)
- [Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG](https://mlsanity.com/paper/2607.17742/) (2026)

## Abstract (arXiv)

While Retrieval-Augmented Generation (RAG) has emerged as an effective approach for addressing the knowledge outdating problem in Large Language Models (LLMs), it still faces a critical challenge: the prevalence of outdated information in knowledge bases. Current research primarily focuses on incorporating up-to-date information, yet the impact of outdated information coexisting in retrieval sources remains inadequately addressed. To bridge this gap, we introduce HoH, the first benchmark specifically designed to evaluate the impact of outdated information on RAG. Our benchmark leverages token-level diff algorithms combined with LLM pipelines to efficiently create a large-scale QA dataset that accurately captures the evolution of temporal knowledge in real-world facts. Through comprehensive experiments, we reveal that outdated information significantly degrades RAG performance in two critical ways: (1) it substantially reduces response accuracy by distracting models from correct information, and (2) it can mislead models into generating potentially harmful outputs, even when current information is available. Current RAG approaches struggle with both retrieval and generation aspects when handling outdated information. These findings highlight the urgent need for innovative solutions to address the temporal challenges in RAG. Our code and data are available at: https://github.com/0russwest0/HoH.