Summary of Towards Ontology-enhanced Representation Learning For Large Language Models, by Francesco Ronzano and Jay Nanavati
Towards Ontology-Enhanced Representation Learning for Large Language Models
by Francesco Ronzano, Jay Nanavati
First submitted to arxiv on: 30 May 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 a novel approach to improve a Large Language Model (LLM) by infusing it with knowledge formalized by a reference ontology. This involves compiling concept definitions using linguistic and structural information from the ontology, and then fine-tuning the LLM using contrastive learning. The approach is demonstrated and evaluated on the biomedical disease ontology MONDO, showing that enhanced LLMs can effectively evaluate sentence similarity in-domain without compromising out-of-domain performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to improve language models by adding knowledge from other fields. It’s like giving a language model a dictionary of important terms and relationships from a specific area, like medicine. This makes the language model more accurate when talking about those topics, but it still works well for other things too. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Large language model
