Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.18722 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916707902160896 |
|---|---|
| author | Nema, Aashutosh Gulati, Samaksh Giakoumakis, Evangelos Thapaliya, Bipana |
| author_facet | Nema, Aashutosh Gulati, Samaksh Giakoumakis, Evangelos Thapaliya, Bipana |
| contents | Recent advances in large language models (LLMs) have led to their popularity across multiple use-cases. However, prompt engineering, the process for optimally utilizing such models, remains approximation-driven and subjective. Most of the current research on prompt engineering focuses on task-specific optimization, while neglecting the behavior of the LLM under consideration during prompt development. This paper introduces MODP -- Multi Objective Directional Prompting, a framework based on two key concepts: 1) multi-objectivity: the importance of considering an LLM's intrinsic behavior as an additional objective in prompt development, and 2) directional prompting: a metrics-driven method for prompt engineering to ensure development of robust and high-precision prompts. We demonstrate the effectiveness of our proposed ideas on a summarization task, using a synthetically created dataset, achieving a 26% performance gain over initial prompts. Finally, we apply MODP to develop prompts for Dell's Next Best Action support tool, which is now in production and is used by more than 10,000 internal support agents and serving millions of customers worldwide. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MODP: Multi Objective Directional Prompting Nema, Aashutosh Gulati, Samaksh Giakoumakis, Evangelos Thapaliya, Bipana Computational Complexity Artificial Intelligence I.2.0; I.2.6; I.2.7; H.3.3 Recent advances in large language models (LLMs) have led to their popularity across multiple use-cases. However, prompt engineering, the process for optimally utilizing such models, remains approximation-driven and subjective. Most of the current research on prompt engineering focuses on task-specific optimization, while neglecting the behavior of the LLM under consideration during prompt development. This paper introduces MODP -- Multi Objective Directional Prompting, a framework based on two key concepts: 1) multi-objectivity: the importance of considering an LLM's intrinsic behavior as an additional objective in prompt development, and 2) directional prompting: a metrics-driven method for prompt engineering to ensure development of robust and high-precision prompts. We demonstrate the effectiveness of our proposed ideas on a summarization task, using a synthetically created dataset, achieving a 26% performance gain over initial prompts. Finally, we apply MODP to develop prompts for Dell's Next Best Action support tool, which is now in production and is used by more than 10,000 internal support agents and serving millions of customers worldwide. |
| title | MODP: Multi Objective Directional Prompting |
| topic | Computational Complexity Artificial Intelligence I.2.0; I.2.6; I.2.7; H.3.3 |
| url | https://arxiv.org/abs/2504.18722 |