Summary of Building Trustworthy Ai: Transparent Ai Systems Via Large Language Models, Ontologies, and Logical Reasoning (transpnet), by Fadi Al Machot et al.
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)
by Fadi Al Machot, Martin Thomas Horsch, Habib Ullah
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Emerging Technologies (cs.ET)
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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 proposes the TranspNet pipeline to address concerns over the lack of transparency in Large Language Models (LLMs). By integrating symbolic AI with LLMs, TranspNet enhances outputs with structured reasoning and formal verification. This approach aims to create AI systems that are accurate, explainable, and trustworthy, aligning with regulatory expectations for transparency and accountability. The pipeline leverages domain expert knowledge, retrieval-augmented generation (RAG), and formal reasoning frameworks like Answer Set Programming (ASP). This solution is suitable for real-world applications where trust is critical. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make artificial intelligence more reliable and understandable. Right now, some AI systems are hard to understand because they work in a way that’s not transparent. The authors propose a new approach called TranspNet that combines different techniques to make AI outputs more structured and easier to verify. This could be important for fields like healthcare and finance where accuracy and trust are crucial. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
