Summary of Enhancing Temporal Sensitivity and Reasoning For Time-sensitive Question Answering, by Wanqi Yang et al.
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
by Wanqi Yang, Yanda Li, Meng Fang, Ling Chen
First submitted to arxiv on: 25 Sep 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 framework for Time-Sensitive Question Answering (TSQA) tackles the challenges of parsing temporal information within questions and identifying time-evolving facts to generate accurate answers. Building on large language models, the framework incorporates Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning to enhance temporal awareness and reasoning. Experimental results on four TSQA datasets show that this approach outperforms existing LLMs in TSQA tasks, a significant step towards bridging the performance gap between machine and human understanding and reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time-Sensitive Question Answering is like trying to answer questions about what happened yesterday or last week. It’s important because we need machines that can understand time and context just like humans do. Right now, big language models are not very good at this. They don’t understand how things change over time, which makes it hard for them to give accurate answers. The new framework tries to fix this by making the model more aware of time and using a special learning method that helps the model reason about time. This is important because it could help machines be better at understanding and answering questions. |
Keywords
» Artificial intelligence » Embedding » Parsing » Question answering » Reinforcement learning
