Summary of Url: Universal Referential Knowledge Linking Via Task-instructed Representation Compression, by Zhuoqun Li et al.
URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression
by Zhuoqun Li, Hongyu Lin, Tianshu Wang, Boxi Cao, Yaojie Lu, Weixiang Zhou, Hao Wang, Zhenyu Zeng, Le Sun, Xianpei Han
First submitted to arxiv on: 24 Apr 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 paper proposes Universal Referential Knowledge Linking (URL) to resolve diversified referential knowledge linking tasks. It builds upon Large Language Models (LLMs) with LLM-driven task-instructed representation compression and multi-view learning approach. The goal is to adapt instruction following and semantic understanding abilities of LLMs to referential knowledge linking. A new benchmark is constructed to evaluate model performance on referential knowledge linking tasks across different scenarios. Experiments demonstrate that URL outperforms previous approaches by a large margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Universal Referential Knowledge Linking (URL) helps computers understand how claims are connected to facts. Right now, computers can only do this for specific tasks like searching the internet or matching words. But in real life, these connections can be very complex and diverse. This paper shows how to create a single model that can handle all types of reference linking. The model uses a special way of compressing information and learning from different perspectives. A new test was created to see how well this model works across different situations. The results show that the new model is much better than previous ones. |
