Summary of Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models, by Himanshu Beniwal et al.
Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models
by Himanshu Beniwal, Dishant Patel, Kowsik Nandagopan D, Hritik Ladia, Ankit Yadav, Mayank Singh
First submitted to arxiv on: 19 Feb 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 study investigates the limitations of Large Language Models (LLMs) in retaining and reasoning about temporal information, a crucial aspect for real-world applications. The researchers experiment with 12 state-of-the-art models on a novel dataset called TempUN, spanning from 10,000 BCE to 2100 CE. They propose six metrics to assess three learning paradigms and find that open-source models exhibit knowledge gaps more frequently, suggesting a trade-off between limited knowledge and incorrect responses. Fine-tuning approaches improve performance, reducing incorrect outputs and identifying information not available in the generations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand human language. But they have trouble remembering things that happen over time, like historical events or stories. This makes it hard for them to be used in real-life situations where understanding what happened before is important. The researchers tested 12 different LLMs on a special dataset of historical and future events, called TempUN. They wanted to see how well the models could remember and understand these events. They also looked at three ways that the models learned from this data: how they got better over time, what they knew at first, and what they didn’t know but should have. The results showed that some models were better than others, and that fine-tuning them made a big difference. |
Keywords
* Artificial intelligence * Fine tuning
