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cloud.google.com·6h ago
AI Research
arXiv CS.AI·28d ago·5 min read
Measuring Pragmatic Influence in Large Language Model Instructions
TL;DR
A new framework for measuring pragmatic framing in large language models (LLMs) reveals that how instructions are phrased can significantly influence model behavior. This study identifies 13 strategies of directive framing that consistently affect directive prioritization across various LLMs.
✦ Why It Matters
Evaluate your LLM prompts to optimize their effectiveness based on pragmatic framing strategies.
Key Takeaways
- Analyze how instruction phrasing affects LLM responses.
- Utilize the taxonomy of 13 framing strategies in prompt design.
- Measure directive prioritization shifts to improve model outputs.
- Consider systematic evaluation of LLMs for better instruction following.
How It Works
The framework separates the context of framing from the task specification, allowing for a clearer understanding of how different phrases influence model behavior. By categorizing 400 examples into 13 strategies, researchers can systematically assess the impact of various framing techniques on directive prioritization.