Prompt engineering boosts small language models for query routing

A new arXiv paper explores the effectiveness of prompt engineering techniques for small language models (SLMs) in performing guarded query routing. The study evaluated 22 SLMs on the GQR-Bench dataset, focusing on their ability to route in-distribution queries and reject out-of-distribution queries. Results indicate that prompt optimization significantly improves the performance of compact models, with Mistral 7B and Qwen3.5 9B achieving notable gains. The research suggests that prompt optimization is a valuable initial step for SLMs in this task, though weaker models might still require weight-level adaptation. AI

IMPACT Demonstrates prompt engineering's effectiveness in enhancing SLM capabilities for specialized tasks like query routing.

RANK_REASON Academic paper detailing model performance and optimization techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• DSPy
• Gemma 3:27B
• GQR-Bench
• Granite 4 Tiny
• Hugging Face
• mistral:7b
• Qwen3.5:9b

• paper
• model release

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