Natural Language Processing
Revisiting Prompt Sensitivity in Large Language Models for Text Classification
This paper investigates prompt sensitivity in LLMs, attributing a significant portion of it to prompt underspecification. It compares underspecified and instruction-specific prompts, finding that the latter improves performance and reduces variance. Linear probing reveals that internal representations are less affected, with issues emerging in final layers. In-context learning and instruction-tuned models are effective mitigation strategies. The study advocates for rigorous prompt design to ensure reliable LLM evaluations.
Executive Impact: Key Findings at a Glance
Our analysis reveals critical insights into LLM prompt sensitivity and effective mitigation strategies for enterprise applications.
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Performance Variance Reduction
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Logit Value Correlation to Accuracy
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Instruction Prompt Gen. Perf. Increase (avg)
Deep Analysis & Enterprise Applications
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Correlation between logit values and prompt quality (LLaMA-3.1)
Enterprise Process Flow
Underspecified Prompt
→
Low Logit Values
→
High Performance Variance
→
Unreliable Classification
→
Instruction Prompt
→
High Logit Values
→
Lower Performance Variance
→
Robust Classification
| Feature | Minimal Prompts | Instruction Prompts |
|---|---|---|
| Task Description | Minimal/None | Specific & Clear |
| Label Constraints | Weak/Absent | Explicitly Defined |
| Performance | Lower, high variance | Higher, lower variance |
| Logit Values | Very small, random distribution | Higher, better distributed |
| Internal Reps Impact | Less direct impact | Consistent robust reps |
| Mitigation Needs | High (ICL, Calibration) | Lower, better alignment |
Impact of In-Context Learning
The study found that in-context learning (ICL), especially when combined with instruction-tuned models, provided the most consistent benefits. It significantly increased performance and reduced standard deviation across both minimal and instruction prompt formats. For minimal prompts, ICL even led to substantial generation accuracy increases, suggesting it effectively addresses the uncertainty caused by underspecification.
- Highest performance increase and standard deviation reduction.
- Effective for both minimal and instruction prompt formats.
- Addresses core underspecification issues, improving model certainty.
- Similar effectiveness to calibration, but without internal model access.
Advanced ROI Calculator
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Your Implementation Roadmap
A structured approach to integrating robust LLM prompting into your enterprise workflows for measurable improvements.
Phase 1: Prompt Design & Testing
Utilize instruction-based prompt formats with explicit task descriptions and label constraints to reduce underspecification from the outset.
Phase 2: Model Selection & Tuning
Prefer instruction-tuned LLM variants over base models, as they inherently align better with structured prompts.
Phase 3: Augmentation & Refinement
Implement in-context learning by providing 2-shot examples per class within the prompt. Consider calibration as an alternative or complementary strategy for further refinement.
Phase 4: Robust Evaluation
Employ both logit and generation evaluation strategies. Use logit analysis to identify high-quality prompts and ensure reliable classification decisions.
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