Summary of Are Llms Prescient? a Continuous Evaluation Using Daily News As the Oracle, by Hui Dai et al.
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
by Hui Dai, Ryan Teehan, Mengye Ren
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses limitations in evaluating Large Language Models (LLMs) by introducing a novel benchmark called Daily Oracle. Unlike existing static evaluation methods, Daily Oracle uses future event prediction to assess LLMs’ temporal generalization and forecasting abilities. The benchmark generates question-answer pairs from daily news, challenging LLMs to predict “future” event outcomes. Our findings show that as pre-training data becomes outdated, LLM performance degrades over time. While Retrieval Augmented Generation (RAG) can enhance prediction accuracy, the performance degradation pattern persists, highlighting the need for continuous model updates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to test how well language models work. Right now, we’re stuck using old tests that don’t account for new models or new information. This paper proposes a new way to test language models by predicting what will happen in the future based on news articles from previous days. The results show that as the information gets older, the language models get worse at making predictions. Even with some improvements, like using other information to help predict the outcome, the language models still don’t do well when the information is really old. This means we need to keep updating our language models to make them better. |
Keywords
» Artificial intelligence » Generalization » Rag » Retrieval augmented generation
