mlsanityData to 14 Oct 2026

MODP: Multi Objective Directional Prompting

arXiv 2504.18722 ↗First posted 25 Apr 2025cs.CC cs.AIcited by 3

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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.

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

14 of 21 references.

References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  3. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
  4. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,154
  5. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  6. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
  7. 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 551
  8. 2020Artificial Intelligence, Values and Alignmentcited by 448
  9. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
  10. 2021Societal Biases in Language Generation: Progress and Challengescited by 202
  11. 2018ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehensioncited by 200
  12. 2023Guiding Large Language Models via Directional Stimulus Promptingcited by 95
2 more
  1. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 47
  2. 2023Revisiting Automated Prompting: Are We Actually Doing Better?cited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A Survey of Context Engineering for Large Language Modelscited by 126
  2. 2025J6: Jacobian-Driven Role Attribution for Multi-Objective Prompt Optimization in LLMscited by 0
  3. 2026Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systemscited by 0