Challenge: Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics.
Approach: They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations.
Outcome: The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science.

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Challenge: Existing closed-ended event forecasting methods are constrained by a limited answer space.
Approach: They introduce OpenForecast, a large-scale open-ended dataset with three open-ending event forecasting tasks and an automatic LLM-based method for complex events.
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Can Language Models Serve as Text-Based World Simulators? (2024.acl-short)

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Challenge: Recent advances in large language models (LLMs) have pointed towards an alternative approach by leveraging the huge amount of knowledge contained in their pre-training datasets.
Approach: They build and use a benchmark to quantify how well text-based simulators can serve as text-driven world simulators.
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Event-Centric Natural Language Processing (2021.acl-tutorials)

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Challenge: This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
Approach: This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks.
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EvEntS ReaLM: Event Reasoning of Entity States via Language Models (2022.emnlp-main)

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Challenge: Existing approaches to model event implications fail to reason about the world, despite their knowledge of physical attributes.
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How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)

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Challenge: Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment.
Approach: They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge.
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DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)

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Challenge: Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model .
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An Efficient Retrieval-Based Method for Tabular Prediction with LLM (2025.coling-main)

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Challenge: Existing methods for tabular prediction rely on extensive pre-training or fine-tuning of LLMs . a retrieval-based approach eliminates the need for training any modules or performing data augmentation .
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Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) are a critical tool for time series analysis and reporting in many fields, including healthcare, finance, climate, and many more.
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How to Contextualize Empirical Data for Risk Analysis with LLMs: A Case Study of Power Outages (2026.findings-eacl)

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Challenge: Large language models (LLMs) are increasingly being considered for high-stakes decision-making, yet their application in statistical risk analysis remains largely underexplored.
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Inferring Events from Time Series using Language Models (2026.acl-long)

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Challenge: Prior work on reasoning about time series in conjunction with natural language has largely overlooked event descriptions and focused on tasks involving just numeric data like trend analysis or anomaly detection.
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