Summary of Timo: Towards Better Temporal Reasoning For Language Models, by Zhaochen Su et al.
Timo: Towards Better Temporal Reasoning for Language Models
by Zhaochen Su, Jun Zhang, Tong Zhu, Xiaoye Qu, Juntao Li, Min Zhang, Yu Cheng
First submitted to arxiv on: 20 Jun 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 A universal framework for Large Language Models (LLMs) to handle various temporal reasoning tasks is proposed, aiming to generalize beyond specific time-sensitive question answering tasks. The authors systematically study 38 temporal reasoning tasks and leverage a mathematical dataset to establish a foundation for temporal reasoning. However, they find that solely focusing on mathematical enhancement falls short of addressing pure temporal reasoning tasks. To address this limitation, the authors propose a self-critic temporal optimization method to enhance the model’s temporal reasoning capabilities without sacrificing general task abilities. The Timo model is developed to excel in temporal reasoning at 7B and 13B scales, outperforming counterpart LLMs by 10.0 and 7.6 average accuracy scores and achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) need to understand time to make sense of the world. Right now, they’re great at answering specific questions about time, but they can’t handle many other types of tasks that involve understanding time. To fix this, researchers are trying to build a special framework that will help LLMs do better with all kinds of temporal reasoning tasks. They studied 38 different tasks and found that some tasks are closely related to math, so they used a big math dataset to test their ideas. However, they also discovered that just focusing on math wasn’t enough – the LLMs still struggled with pure temporal reasoning tasks. To overcome this limitation, the researchers came up with a new way to optimize the models’ performance without sacrificing their ability to handle other tasks. The result is a special model called Timo that’s really good at understanding time and outperforms other similar models. |
Keywords
» Artificial intelligence » Optimization » Question answering
