Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle

• URL: http://arxiv.org/abs/2411.08324v1
• Date: Wed, 13 Nov 2024 04:20:20 GMT
• Title: Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
• Authors: Hui Dai, Ryan Teehan, Mengye Ren,
• Abstract summary: We propose using future event prediction as a continuous evaluation method to assess Large Language Models' temporal generalization abilities. Our benchmark, Daily Oracle, automatically generates question-answer pairs from daily news, challenging LLMs to predict "future" event outcomes.
• Score: 13.192628306219248
• License:
• Abstract: Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data. These benchmarks also fall short in assessing how LLM performance changes over time, as they consist of static questions without a temporal dimension. To address these limitations, we propose using future event prediction as a continuous evaluation method to assess LLMs' temporal generalization and forecasting abilities. Our benchmark, Daily Oracle, automatically generates question-answer (QA) pairs from daily news, challenging LLMs to predict "future" event outcomes. Our findings reveal that as pre-training data becomes outdated, LLM performance degrades over time. While Retrieval Augmented Generation (RAG) has the potential to enhance prediction accuracy, the performance degradation pattern persists, highlighting the need for continuous model updates.

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