Prompt Chaining Boosts SLM Dialogue Quality to Rival Larger Models
Published:Jan 6, 2026 05:00
•1 min read
•ArXiv NLP
Analysis
This research demonstrates a promising method for improving the performance of smaller language models in open-domain dialogue through multi-dimensional prompt engineering. The significant gains in diversity, coherence, and engagingness suggest a viable path towards resource-efficient dialogue systems. Further investigation is needed to assess the generalizability of this framework across different dialogue domains and SLM architectures.
Key Takeaways
- •Multi-dimensional prompt chaining enhances SLM dialogue quality.
- •Llama-2-7B achieves comparable performance to Llama-2-70B and GPT-3.5 Turbo with the framework.
- •The framework improves response diversity, coherence, and engagingness by up to 29%.
Reference
“Overall, the findings demonstrate that carefully designed prompt-based strategies provide an effective and resource-efficient pathway to improving open-domain dialogue quality in SLMs.”