# Analyzing the Role of Context in Forecasting with Large Language Models

arXiv: https://arxiv.org/abs/2501.06496 · HTML: https://mlsanity.com/paper/2501.06496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data](https://mlsanity.com/paper/2005.00792/) (2020)

## Built on by (most cited first, 3 of 3)

- [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025)
- [AIA Forecaster: Technical Report](https://mlsanity.com/paper/2511.07678/) (2025)
- [Detecting Future-related Contexts of Entity Mentions](https://mlsanity.com/paper/2502.15332/) (2025)

## Abstract (arXiv)

This study evaluates the forecasting performance of recent language models (LLMs) on binary forecasting questions. We first introduce a novel dataset of over 600 binary forecasting questions, augmented with related news articles and their concise question-related summaries. We then explore the impact of input prompts with varying level of context on forecasting performance. The results indicate that incorporating news articles significantly improves performance, while using few-shot examples leads to a decline in accuracy. We find that larger models consistently outperform smaller models, highlighting the potential of LLMs in enhancing automated forecasting.