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

Created by
  • Haebom

Author

Hui Dai, Ryan Teehan, Mengye Ren

Outline

In this paper, we propose a continuous evaluation method for predicting future events based on daily news to solve __T4768__ of large-scale language model (LLM) evaluation benchmarks. We evaluate the temporal generalization and predictive ability of LLM using automatically generated question-answer (QA) pairs on the benchmark called 'Daily Oracle'. Our results show that the performance of LLM deteriorates as the pre-training data gets older, and the degradation persists even when augmented retrieval generation (RAG) is used, emphasizing the need for continuous model updating. The code and data can be found in __T4767_____ .

Takeaways, Limitations

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Takeaways:
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A novel continuous assessment method is presented to assess the temporal generalization and predictive ability of LLM.
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Identifying the correlation between staleness of pre-training data and degradation of LLM performance.
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Despite the use of RAG, the need for continuous updating of the LLM is emphasized.
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Presenting the possibility of continuous monitoring of LLM performance via Daily Oracle Benchmarks.
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Limitations:
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Consideration needs to be given to the long-term stability and maintenance of the Daily Oracle Benchmark.
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Generalizability verification is needed for various types of LLMs and datasets.
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Further research is needed to maximize the effectiveness of RAG.
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Difficulty in ensuring objectivity in evaluation due to uncertainty in future predictions.
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