# MODP: Multi Objective Directional Prompting

arXiv: https://arxiv.org/abs/2504.18722 · HTML: https://mlsanity.com/paper/2504.18722/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://mlsanity.com/paper/2310.03693/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (2021)
- [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://mlsanity.com/paper/1810.12885/) (2018)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://mlsanity.com/paper/2302.11520/) (2023)
- [A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks](https://mlsanity.com/paper/2407.12994/) (2024)
- [Revisiting Automated Prompting: Are We Actually Doing Better?](https://mlsanity.com/paper/2304.03609/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [J6: Jacobian-Driven Role Attribution for Multi-Objective Prompt Optimization in LLMs](https://mlsanity.com/paper/2508.12086/) (2025)
- [Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems](https://mlsanity.com/paper/2602.20379/) (2026)

## Abstract (arXiv)

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.