A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910657841987584 |
|---|---|
| author | Hannah, George Sousa, Rita T. Dasoulas, Ioannis d'Amato, Claudia |
| author_facet | Hannah, George Sousa, Rita T. Dasoulas, Ioannis d'Amato, Claudia |
| contents | With the recent surge in popularity of Large Language Models (LLMs), there is the rising risk of users blindly trusting the information in the response, even in cases where the LLM recommends actions that have potential legal implications and this may put the user in danger. We provide an empirical analysis on multiple existing LLMs showing the urgency of the problem. Hence, we propose a short-term solution consisting in an approach for isolating these legal issues through prompt re-engineering. We further analyse the outcomes but also the limitations of the prompt engineering based approach and we highlight the need of additional resources for fully solving the problem We also propose a framework powered by a legal knowledge graph (KG) to generate legal citations for these legal issues, enriching the response of the LLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers Hannah, George Sousa, Rita T. Dasoulas, Ioannis d'Amato, Claudia Artificial Intelligence I.2.1 With the recent surge in popularity of Large Language Models (LLMs), there is the rising risk of users blindly trusting the information in the response, even in cases where the LLM recommends actions that have potential legal implications and this may put the user in danger. We provide an empirical analysis on multiple existing LLMs showing the urgency of the problem. Hence, we propose a short-term solution consisting in an approach for isolating these legal issues through prompt re-engineering. We further analyse the outcomes but also the limitations of the prompt engineering based approach and we highlight the need of additional resources for fully solving the problem We also propose a framework powered by a legal knowledge graph (KG) to generate legal citations for these legal issues, enriching the response of the LLM. |
| title | A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers |
| topic | Artificial Intelligence I.2.1 |
| url | https://arxiv.org/abs/2410.15064 |