# Is Your LLM Outdated? A Deep Look at Temporal Generalization

arXiv: https://arxiv.org/abs/2405.08460 · HTML: https://mlsanity.com/paper/2405.08460/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 17 of 17)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://mlsanity.com/paper/2310.03214/) (2023)
- [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)
- [Skywork: A More Open Bilingual Foundation Model](https://mlsanity.com/paper/2310.19341/) (2023)
- [Approaching Human-Level Forecasting with Language Models](https://mlsanity.com/paper/2402.18563/) (2024)
- [Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy](https://mlsanity.com/paper/2402.19379/) (2024)
- [Forecasting Future World Events with Neural Networks](https://mlsanity.com/paper/2206.15474/) (2022)
- [A/B/n Testing with Control in the Presence of Subpopulations](https://mlsanity.com/paper/2110.15573/) (2021)
- [Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies](https://mlsanity.com/paper/2312.01509/) (2023)
- [Can Base ChatGPT be Used for Forecasting without Additional Optimization?](https://mlsanity.com/paper/2404.07396/) (2024)

## Built on by (most cited first, 26 of 26)

- [LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena](https://mlsanity.com/paper/2510.17638/) (2025)
- [Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle](https://mlsanity.com/paper/2411.08324/) (2024)
- [MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants](https://mlsanity.com/paper/2507.01887/) (2025)
- [ProveRAG: Provenance-Driven Vulnerability Analysis with Automated Retrieval-Augmented LLMs](https://mlsanity.com/paper/2410.17406/) (2024)
- [Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models](https://mlsanity.com/paper/2509.04304/) (2025)
- [LLMLagBench: Identifying Temporal Training Boundaries in Large Language Models](https://mlsanity.com/paper/2511.12116/) (2025)
- [Safety is Contextual, LLM-Judges Are Not: Navigating the Rigid Priors of Evaluators](https://mlsanity.com/paper/2606.07874/) (2026)
- [TiMoE: Time-Aware Mixture of Language Experts](https://mlsanity.com/paper/2508.08827/) (2025)
- [All Leaks Count, Some Count More: Interpretable Temporal Contamination Detection and Mitigation in LLM Backtesting](https://mlsanity.com/paper/2602.17234/) (2026)
- [WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning](https://mlsanity.com/paper/2606.11816/) (2026)
- [To Copy or Not to Copy: Copying Is Easier to Induce Than Recall](https://mlsanity.com/paper/2601.12075/) (2026)
- [Evaluating Temporal Consistency in Multi-Turn Language Models](https://mlsanity.com/paper/2604.23051/) (2026)
- [Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering](https://mlsanity.com/paper/2605.23497/) (2026)
- [Towards Knowledge Alignment in Code LLMs: Contrastive Unlearning for Evolving APIs](https://mlsanity.com/paper/2606.30810/) (2026)
- [Temporal Generalization: A Reality Check](https://mlsanity.com/paper/2509.23487/) (2025)
- [Impacts of Racial Bias in Historical Training Data for News AI](https://mlsanity.com/paper/2512.16901/) (2025)
- [LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.06198/) (2026)
- [Temporal Flattening in LLM-Generated Text: Comparing Human and LLM Writing Trajectories](https://mlsanity.com/paper/2604.12097/) (2026)
- [Decentralized Aggregation of LLM Predictions via Wagering Mechanisms](https://mlsanity.com/paper/2607.04389/) (2026)
- [Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments](https://mlsanity.com/paper/2608.00419/) (2026)
- [Aligning Biomedical Texts and Knowledge Graphs: A Systematic Comparison of Lightweight Alignment Strategies](https://mlsanity.com/paper/2608.23214/) (2026)
- [Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation](https://mlsanity.com/paper/2609.08943/) (2026)
- [KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologisms](https://mlsanity.com/paper/2609.19916/) (2026)
- [Uncheatable Eval: Dynamic Compression-Based Evaluation of Language Models](https://mlsanity.com/paper/2609.27510/) (2026)
- [PALM: Point-in-Time Adaptation for Financial Language Models](https://mlsanity.com/paper/2609.30316/) (2026)
- [Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts](https://mlsanity.com/paper/2609.33257/) (2026)

## Abstract (arXiv)

The rapid advancement of Large Language Models (LLMs) has led to the development of benchmarks that consider temporal dynamics, however, there remains a gap in understanding how well these models can generalize across temporal contexts due to the inherent dynamic nature of language and information. This paper introduces the concept of temporal generalization in LLMs, including bias in past and future generalizations. Then we introduce FreshBench, a new evaluation framework that employs fresh text and event prediction for assessing LLMs' temporal adaptability, ensuring the evaluation process free from data leakage and subjective bias. The experiment shows significant temporal biases and a decline in performance over time. Our findings reveal that powerful models, while initially superior, tend to decline more rapidly in future generalization. Additionally, powerful open-source models demonstrate better long-term adaptability compared to their closed-source counterparts. Our code is available at https://github.com/FreedomIntelligence/FreshBench.