Analyzing the Role of Context in Forecasting with Large Language Models
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910780718317568 |
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| author | Mutschlechner, Gerrit Jatowt, Adam |
| author_facet | Mutschlechner, Gerrit Jatowt, Adam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing the Role of Context in Forecasting with Large Language Models Mutschlechner, Gerrit Jatowt, Adam Computation and Language Information Retrieval 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. |
| title | Analyzing the Role of Context in Forecasting with Large Language Models |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2501.06496 |