Summary of Can Structured Data Reduce Epistemic Uncertainty?, by Shriram M S et al.
Can Structured Data Reduce Epistemic Uncertainty?
by Shriram M S, Sushmitha S, Gayathri K S, Shahina A
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 framework that leverages ontology alignment to improve the learning process of deep learning models. The authors fine-tune deep learning models using ontologies and demonstrate that these models learn downstream tasks at a higher rate with better performance on sequential classification tasks compared to native versions. Additionally, the work showcases how subsumption mappings retrieved during ontology alignment enhance Retrieval-Augmented Generation in Large Language Models (LLMs). The results show significant improvements in contextual similarity (8.97%) and factual accuracy (1%), leading to a reduced Hallucination Index of 4.847%. This approach is applied to LLMs, highlighting its potential applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research improves how computers learn new skills by using special connections called ontologies. The team shows that when they use these connections to help deep learning models, the models can learn faster and do better on certain tasks. They also show that this approach can help large language models generate more accurate and relevant responses. The results are impressive, with a significant increase in how well the computer understands the context and a small but important decrease in making up facts it wasn’t told. |
Keywords
» Artificial intelligence » Alignment » Classification » Deep learning » Hallucination » Retrieval augmented generation
