# Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding

arXiv: https://arxiv.org/abs/2406.02472 · HTML: https://mlsanity.com/paper/2406.02472/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [L-Eval: Instituting Standardized Evaluation for Long Context Language Models](https://mlsanity.com/paper/2307.11088/) (2023)
- [ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding](https://mlsanity.com/paper/2305.14196/) (2023)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal](https://mlsanity.com/paper/2005.10070/) (2020)
- [ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data](https://mlsanity.com/paper/2005.00792/) (2020)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Examining the State-of-the-Art in News Timeline Summarization](https://mlsanity.com/paper/2005.10107/) (2020)
- [STARC: Structured Annotations for Reading Comprehension](https://mlsanity.com/paper/2004.14797/) (2020)
- [Conditional Generation of Temporally-ordered Event Sequences](https://mlsanity.com/paper/2012.15786/) (2020)
- [Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings](https://mlsanity.com/paper/2101.11059/) (2021)
- [SCStory: Self-supervised and Continual Online Story Discovery](https://mlsanity.com/paper/2312.03725/) (2023)
- [A Diffusion Model for Event Skeleton Generation](https://mlsanity.com/paper/2305.17458/) (2023)

## Built on by (most cited first, 23 of 23)

- [MIRAI: Evaluating LLM Agents for Event Forecasting](https://mlsanity.com/paper/2407.01231/) (2024)
- [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)
- [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024)
- [Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement](https://mlsanity.com/paper/2506.03541/) (2025)
- [Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents](https://mlsanity.com/paper/2502.17967/) (2025)
- [ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains](https://mlsanity.com/paper/2410.09870/) (2024)
- [MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models](https://mlsanity.com/paper/2510.19457/) (2025)
- [T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval](https://mlsanity.com/paper/2508.01680/) (2025)
- [Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models](https://mlsanity.com/paper/2409.06223/) (2024)
- [MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models](https://mlsanity.com/paper/2408.04388/) (2024)
- [AttackSeqBench: Benchmarking the Capabilities of LLMs for Attack Sequences Understanding](https://mlsanity.com/paper/2503.03170/) (2025)
- [Reading Between the Timelines: RAG for Answering Diachronic Questions](https://mlsanity.com/paper/2507.22917/) (2025)
- [DTELS: Towards Dynamic Granularity of Timeline Summarization](https://mlsanity.com/paper/2411.09297/) (2024)
- [LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams](https://mlsanity.com/paper/2504.17366/) (2025)
- [ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting](https://mlsanity.com/paper/2601.17065/) (2026)
- [Evaluating List Construction and Temporal Understanding capabilities of Large Language Models](https://mlsanity.com/paper/2506.21783/) (2025)
- [Temporal Text Classification with Large Language Models](https://mlsanity.com/paper/2603.11295/) (2026)
- [Event-Centric Human Value Understanding in News-Domain Texts: An Actor-Conditioned, Multi-Granularity Benchmark](https://mlsanity.com/paper/2603.17838/) (2026)
- [Can Structural Cues Save LLMs? Evaluating Language Models in Massive Document Streams](https://mlsanity.com/paper/2603.19250/) (2026)
- [Decentralized Aggregation of LLM Predictions via Wagering Mechanisms](https://mlsanity.com/paper/2607.04389/) (2026)
- [From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models](https://mlsanity.com/paper/2607.27654/) (2026)
- [Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts](https://mlsanity.com/paper/2609.33257/) (2026)

## Abstract (arXiv)

The digital landscape is rapidly evolving with an ever-increasing volume of online news, emphasizing the need for swift and precise analysis of complex events. We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE). This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE, characterized by their key points and timestamps. We establish a benchmark, named TCELongBench, to evaluate the proficiency of LLMs in handling temporal dynamics and understanding extensive text. This benchmark encompasses three distinct tasks - reading comprehension, temporal sequencing, and future event forecasting. In the experiment, we leverage retrieval-augmented generation (RAG) method and LLMs with long context window to deal with lengthy news articles of TCE. Our findings indicate that models with suitable retrievers exhibit comparable performance with those utilizing long context window.