LELA: A Zero-Shot LLM-Based Entity Linking Framework
A practical Python library has been developed by researchers to enhance LELA, a modular and domain-agnostic method for entity disambiguation based on LLM. This library incorporates zero-shot Named Entity Recognition (NER), offering a comprehensive end-to-end solution for entity linking in real-world applications. Experimental findings confirm the effectiveness and reliability of LELA in various entity linking scenarios. Additionally, a demonstration is available, enabling users to evaluate the system using their own text inputs.
Key facts
- LELA is an end-to-end LLM-based entity linking framework.
- It supports zero-shot domain adaptation.
- The framework integrates zero-shot Named Entity Recognition (NER).
- It is provided as a practical Python library.
- Experimental results show performance and robustness.
- A demo is available for user input testing.
- The paper is categorized under Computer Science > Artificial Intelligence.
- The submission is on arXiv.
Entities
Institutions
- arXiv