Summary of A Prompt Engineering Approach and a Knowledge Graph Based Framework For Tackling Legal Implications Of Large Language Model Answers, by George Hannah et al.
A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers
by George Hannah, Rita T. Sousa, Ioannis Dasoulas, Claudia d’Amato
First submitted to arxiv on: 19 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 addresses the pressing concern of users blindly trusting Large Language Models’ (LLMs) recommendations, which may have severe legal implications. The study empirically examines multiple existing LLMs and finds that prompt re-engineering can be an effective short-term solution to isolate legal issues. However, this approach has limitations, highlighting the need for additional resources to fully solve the problem. To mitigate these risks, a framework powered by a legal knowledge graph (KG) is proposed to generate legal citations for legal issues, enriching LLM responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are popular, but users might blindly trust their recommendations, even if they suggest actions with serious legal consequences. This could put people in danger! To fix this problem, researchers looked at many existing language models and found that making small changes to the questions asked can help keep the language model from suggesting bad ideas. However, this isn’t a complete solution yet, and we need more work to make sure it’s safe for everyone. |
Keywords
» Artificial intelligence » Knowledge graph » Language model » Prompt
