Summary of Chroknowledge: Unveiling Chronological Knowledge Of Language Models in Multiple Domains, by Yein Park et al.
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
by Yein Park, Chanwoong Yoon, Jungwoo Park, Donghyeon Lee, Minbyul Jeong, Jaewoo Kang
First submitted to arxiv on: 13 Oct 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 ChroKnowBench benchmark dataset aims to evaluate large language models’ (LLMs) chronological knowledge accumulation in three key aspects: multiple domains, time dependency, and temporal state. The benchmark distinguishes between evolving knowledge (e.g., personal history, scientific discoveries) and constant knowledge (e.g., mathematical truths). A novel framework, ChroKnowledge, is presented for evaluating LLMs’ non-parametric chronological knowledge. Evaluation results show that LLMs’ ability to elicit temporal knowledge varies depending on training data format, with partial recall or cutoff at temporal boundaries. To overcome these limitations, the authors introduce the ChroKnowPrompt, a step-by-step prompting approach that successfully recalls objects across various LLMs and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are changing many aspects of our lives. But have you ever wondered how they remember things over time? This paper helps us understand this by creating a special test dataset called ChroKnowBench. It looks at how well the model remembers information that changes over time, like scientific discoveries or laws being updated. The paper also presents a new way to test these models’ memory and shows that their ability to remember things depends on the type of data they were trained on. This research can help us make better language models that are more accurate and helpful in our daily lives. |
Keywords
» Artificial intelligence » Prompting » Recall
