# DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs

arXiv: https://arxiv.org/abs/2404.08700 · HTML: https://mlsanity.com/paper/2404.08700/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [OLMo: Accelerating the Science of Language Models](https://mlsanity.com/paper/2402.00838/) (2024)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Dissecting Recall of Factual Associations in Auto-Regressive Language Models](https://mlsanity.com/paper/2304.14767/) (2023)
- [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://mlsanity.com/paper/2402.00159/) (2024)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [Can We Edit Factual Knowledge by In-Context Learning?](https://mlsanity.com/paper/2305.12740/) (2023)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models](https://mlsanity.com/paper/2301.04213/) (2023)
- [Evaluating the Ripple Effects of Knowledge Editing in Language Models](https://mlsanity.com/paper/2307.12976/) (2023)
- [Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?](https://mlsanity.com/paper/2308.10168/) (2023)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models](https://mlsanity.com/paper/2204.14211/) (2022)
- [Crawling the Internal Knowledge-Base of Language Models](https://mlsanity.com/paper/2301.12810/) (2023)
- [Calibrating Large Language Models with Sample Consistency](https://mlsanity.com/paper/2402.13904/) (2024)
- [ALCUNA: Large Language Models Meet New Knowledge](https://mlsanity.com/paper/2310.14820/) (2023)
- [A Survey on Knowledge Editing of Neural Networks](https://mlsanity.com/paper/2310.19704/) (2023)
- [Emptying the Ocean with a Spoon: Should We Edit Models?](https://mlsanity.com/paper/2310.11958/) (2023)
- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)

## Built on by (most cited first, 24 of 24)

- [Knowledge Updating? No More Model Editing! Just Selective Contextual Reasoning](https://mlsanity.com/paper/2503.05212/) (2025)
- [LLMs as Repositories of Factual Knowledge: Limitations and Solutions](https://mlsanity.com/paper/2501.12774/) (2025)
- [Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons](https://mlsanity.com/paper/2502.02988/) (2025)
- [G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge](https://mlsanity.com/paper/2509.24276/) (2025)
- [Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems](https://mlsanity.com/paper/2511.01268/) (2025)
- [When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations](https://mlsanity.com/paper/2505.24449/) (2025)
- [MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models](https://mlsanity.com/paper/2510.19457/) (2025)
- [Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.13272/) (2025)
- [From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs](https://mlsanity.com/paper/2507.22716/) (2025)
- [LLMLagBench: Identifying Temporal Training Boundaries in Large Language Models](https://mlsanity.com/paper/2511.12116/) (2025)
- [What Does Loss Optimization Actually Teach, If Anything? Knowledge Dynamics in Continual Pre-training of LLMs](https://mlsanity.com/paper/2601.03858/) (2026)
- [TiMoE: Time-Aware Mixture of Language Experts](https://mlsanity.com/paper/2508.08827/) (2025)
- [An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs](https://mlsanity.com/paper/2508.21024/) (2025)
- [Large Language Models Lack Temporal Awareness of Medical Knowledge](https://mlsanity.com/paper/2605.13045/) (2026)
- [Factual Knowledge in Language Models: Robustness and Anomalies under Simple Temporal Context Variations](https://mlsanity.com/paper/2502.01220/) (2025)
- [ExplicitLM: Decoupling Knowledge from Parameters via Explicit Memory Banks](https://mlsanity.com/paper/2511.01581/) (2025)
- [Benchmarking and Rethinking Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2505.18690/) (2025)
- [Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models](https://mlsanity.com/paper/2508.02045/) (2025)
- [Frame-Guided Synthetic Claim Generation for Automatic Fact-Checking Using High-Volume Tabular Data](https://mlsanity.com/paper/2601.17232/) (2026)
- [V-DyKnow: A Dynamic Benchmark for Time-Sensitive Knowledge in Vision Language Models](https://mlsanity.com/paper/2603.16581/) (2026)
- [Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence](https://mlsanity.com/paper/2606.00570/) (2026)
- [Dense Clinical Contrasts Enhance Medical Knowledge Updating in Large Language Models](https://mlsanity.com/paper/2608.30405/) (2026)
- [SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection](https://mlsanity.com/paper/2609.09349/) (2026)
- [Identifying Temporal Features within Transcoders for Time Sensitive Factual Recall](https://mlsanity.com/paper/2609.33183/) (2026)

## Abstract (arXiv)

LLMs acquire knowledge from massive data snapshots collected at different timestamps. Their knowledge is then commonly evaluated using static benchmarks. However, factual knowledge is generally subject to time-sensitive changes, and static benchmarks cannot address those cases. We present an approach to dynamically evaluate the knowledge in LLMs and their time-sensitiveness against Wikidata, a publicly available up-to-date knowledge graph. We evaluate the time-sensitive knowledge in twenty-four private and open-source LLMs, as well as the effectiveness of four editing methods in updating the outdated facts. Our results show that 1) outdatedness is a critical problem across state-of-the-art LLMs; 2) LLMs output inconsistent answers when prompted with slight variations of the question prompt; and 3) the performance of the state-of-the-art knowledge editing algorithms is very limited, as they can not reduce the cases of outdatedness and output inconsistency.