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Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction
arXiv – CS AI|Rafael R. Baptista, Andr\'e de Lima Salgado, Ricardo V. Godoy, Marcelo Becker, Thiago Boaventura, Gustavo J. G. Lahr||7 views
🤖AI Summary
Researchers benchmarked small language models (SLMs) for leader-follower role classification in human-robot interaction, finding that fine-tuned Qwen2.5-0.5B achieves 86.66% accuracy with 22.2ms latency. The study demonstrates SLMs can effectively handle real-time role assignment for resource-constrained robots, though performance degrades with increased dialogue complexity.
Key Takeaways
- →Fine-tuned small language models achieve 86.66% accuracy in leader-follower role classification for human-robot interaction.
- →Zero-shot fine-tuning significantly outperforms baseline and prompt-engineered approaches for role assignment tasks.
- →Small language models maintain low latency (22.2ms per sample) suitable for real-time edge deployment.
- →Performance degrades in one-shot modes due to increased context length challenging model capacity.
- →SLMs offer viable alternative to large language models for resource-constrained mobile and assistive robots.
#small-language-models#human-robot-interaction#fine-tuning#edge-computing#role-classification#qwen#zero-shot-learning#robotics#ai-research
Read Original →via arXiv – CS AI
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