Exploring Modular Prompt Design for Emotion and Mental Health Recognition

Research Background and Problem

• Identified Problems or Challenges
  The authors highlight that prompt design is critical in tasks involving the classification of emotional and mental health states using large language models (LLMs). However, existing studies lack a systematic analysis of the prompt design space and the impact of each component. This has resulted in fragmented approaches, posing challenges to standardized evaluation and reproducibility in prompt design.
  Additionally, developers face significant difficulties in optimizing prompts to enhance system performance, primarily due to the complexity of the design space and the unpredictability of LLM behavior.

• Why It Matters
  Tasks related to emotional and mental health analysis have widespread applications in sensitive areas such as psychological counseling, emotion tracking, and risk signal detection. These tasks are crucial for improving the quality of public health services and supporting expert decision-making. However, significant performance variations often depend on prompt design, directly affecting the accuracy and reliability of these tasks.

• Research Motivation and Related Work
  The motivation for this research is to bridge the gap between theoretical and practical analyses of the prompt design space for LLMs and to provide a systematic, modular prompt design framework that promotes transparency and reproducibility. Related work includes prompt optimization techniques (e.g., chain-of-thought reasoning and in-context learning) and the exploration of emotion-oriented LLM prompt strategies.

Solution

• Proposed Method or Solution
  The authors propose a modular prompt design approach that decomposes prompts into six key components: Persona, Task Instruction, N-shot Examples, Input, Output, and Template. This modular design provides a framework for systematically evaluating and optimizing prompts.

• Innovative Aspects of the Solution

  • Offers a clear breakdown of prompt components, making it easier for designers to understand and adjust the impact of each component on task performance.
  • Develops three distinct styles for task instructions (clear and direct, emotional description, technical analysis) for comparative evaluation.
  • Introduces the first systematic framework to refine the interaction and performance impact of components, promoting flexibility and reusability in prompt design.

• Implementation Steps and Key Techniques

  1. Literature Analysis: Extracted prompt examples from 30 existing studies and conducted thematic analysis to identify key components.
  2. Modular Design: Decomposed the extracted prompts into six modules to prepare for systematic evaluation.
  3. Component Evaluation: Selected two modules (Persona and Task Instruction) for experiments and systematically evaluated their performance differences using five datasets and four LLMs.
  4. Component Interaction Analysis: Explored the synergy between Persona and Task Instruction by comparing different configuration combinations to assess their performance impact.

Research Findings

• Specific Findings

  • Impact of the Persona Component: Large models (e.g., GPT-4o) showed significant performance improvements with the use of Persona, particularly in handling complex mental health classification tasks. Smaller models like Mistral-7B exhibited inconsistent performance.
  • Impact of the Task Instruction Component: Clear and direct instructions were more effective for simple tasks (e.g., binary classification), while the emotional description style performed better in complex tasks (e.g., suicide risk assessment).
  • Interaction Analysis: Although Persona and Task Instruction individually improved model performance, their combination did not always lead to further improvements and sometimes resulted in counteracting effects.

• Comparison with Existing Solutions and Advantages

  • Provides a modular, flexible prompt design framework for sensitive domains, representing a significant improvement over fragmented existing methods.
  • Standardizes and enhances the reusability of the prompt optimization process.
  • Offers detailed component impact analysis, guiding task designers to make optimal choices based on practical applications.

• Experimental or Evaluation Results

  • Modular prompt design significantly improved LLM classification performance across various emotional and mental health tasks, revealing the specific performance contributions of individual and combined components.
  • The GPT-4o model demonstrated superior accuracy and macro F1 scores across most datasets, validating the practicality of modular prompt design.

• Limitations and Future Directions

  • Did not comprehensively evaluate all modules and their possible combinations; future research should explore more complex component interactions.
  • Open-source models may exhibit biased results due to overlaps between training and test datasets; future studies should enhance deduplication checks to ensure dataset independence.
  • Scalable automated prompt optimization tools have yet to be developed; future work could integrate modular design to improve efficiency and adaptability.

Through the systematic analysis and deployment of the modular prompt design framework, the authors provide a viable pathway for optimizing prompts in emotional and mental health tasks, laying a foundation for further exploration of prompt optimization mechanisms.