Summary of Is Your Llm Outdated? Evaluating Llms at Temporal Generalization, by Chenghao Zhu and Nuo Chen and Yufei Gao and Yunyi Zhang and Prayag Tiwari and Benyou Wang
Is Your LLM Outdated? Evaluating LLMs at Temporal Generalization
by Chenghao Zhu, Nuo Chen, Yufei Gao, Yunyi Zhang, Prayag Tiwari, Benyou Wang
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed study examines the limitations of traditional evaluation methodologies for Large Language Models (LLMs) in capturing their performance in ever-changing real-world scenarios. The research highlights significant temporal biases in LLMs, which struggle with understanding, predicting, and generating text relevant to past, present, and future contexts. To address this issue, the authors propose an evaluation framework that dynamically generates benchmarks from recent real-world predictions. Experiments demonstrate a decline in performance over time, emphasizing the need for improved training and updating processes to enhance adaptability and reduce biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate human language. But they’re not perfect – they often struggle with understanding things that happened a long time ago or will happen in the future. This makes it hard to use them for important tasks like helping us make decisions about what’s happening now or predicting what might happen later. To fix this problem, scientists created a new way to test how well these models work, using real-world examples from recent news articles and social media posts. They found that even the best models get worse over time, which means we need to find ways to keep them learning and improving. |
