Summary of Onenet: a Fine-tuning Free Framework For Few-shot Entity Linking Via Large Language Model Prompting, by Xukai Liu et al.
OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting
by Xukai Liu, Ye Liu, Kai Zhang, Kehang Wang, Qi Liu, Enhong Chen
First submitted to arxiv on: 10 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 This paper presents OneNet, a novel framework for few-shot entity linking, which uses Large Language Models (LLMs) to achieve high performance without requiring fine-tuning. The proposed approach consists of three components: an entity reduction processor that simplifies inputs, a dual-perspective entity linker that combines contextual cues and prior knowledge, and an entity consensus judger that employs a unique consistency algorithm to reduce hallucinations. OneNet outperforms current state-of-the-art entity linking methods across seven benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OneNet is a new way to link ambiguous words in text to specific things we know about using only a few examples. It uses special language models without needing extra training. The model has three parts: one that simplifies the input, one that links the word to what it means, and one that makes sure the linking is correct. This approach does better than others on seven tests. |
Keywords
» Artificial intelligence » Entity linking » Few shot » Fine tuning
