Summary of Sparql Generation: An Analysis on Fine-tuning Openllama For Question Answering Over a Life Science Knowledge Graph, by Julio C. Rangel et al.
SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph
by Julio C. Rangel, Tarcisio Mendes de Farias, Ana Claudia Sima, Norio Kobayashi
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Databases (cs.DB); Information Retrieval (cs.IR)
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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 a novel approach to fine-tuning Large Language Models (LLMs) for question answering over Knowledge Graphs. Specifically, it addresses the scarcity of training data for translating questions into corresponding SPARQL queries by proposing an end-to-end data augmentation method. The authors evaluate this approach on the Bgee gene expression knowledge graph and demonstrate a significant improvement in model performance when incorporating semantic “clues” such as meaningful variable names and inline comments. This work has implications for developing Question Answering Systems over Life Science Knowledge Graphs, leveraging the power of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special language models to help answer questions based on large amounts of information stored in a database. Right now, it’s hard to train these models because we don’t have enough examples of how to translate questions into the right format for the database. The researchers came up with a new way to create more examples by changing and adding to existing questions. They tested this approach using data from the Bgee gene expression knowledge graph and found that it made their language model much better at answering questions. This could be useful for people working in life sciences who want to use these models to help answer complex biological questions. |
Keywords
» Artificial intelligence » Data augmentation » Fine tuning » Knowledge graph » Language model » Question answering
