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AI Research
arXiv CS.AI·14d ago·6 min read
Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models
TL;DR
Controlled experiments reveal that prompt design significantly impacts instruction adherence and hallucination in large language models. Key findings show that increasing instruction count leads to a collapse in response quality, while context length affects recall accuracy variably across formats.
✦ Why It Matters
Evaluate your prompt designs to ensure they maximize instruction adherence and minimize hallucination in your applications.
Key Takeaways
- Limit simultaneous instructions to below 80 for better compliance.
- Test different formats to find the most effective for your model.
- Monitor context length to maintain recall accuracy.
- Avoid markdown formatting as it may not provide advantages.
How It Works
The study systematically varied instruction count, format, and context length to assess their effects on model performance. By using a controlled synthetic corpus, the researchers could isolate the impact of each factor on instruction adherence and recall accuracy.