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[2602.23312] Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction
Summary
This paper evaluates the effectiveness of small language models (SLMs) in leader-follower interactions, comparing zero-shot and one-shot adaptation strategies for role classification in human-robot communication.
Why It Matters
The research addresses the challenges of real-time role assignment in resource-constrained mobile robots, highlighting the potential of small language models to enhance human-robot interaction. By systematically evaluating adaptation strategies, the findings can inform future developments in robotics and AI communication.
Key Takeaways
- Zero-shot fine-tuning of small language models achieves high classification accuracy (86.66%) with low latency (22.2 ms).
- One-shot adaptation shows performance degradation due to increased context length, indicating architectural limitations.
- The study introduces a novel dataset for leader-follower communication, enhancing the evaluation of small language models.
- Fine-tuned small language models provide effective solutions for direct role assignment in human-robot interactions.
- Trade-offs exist between dialogue complexity and classification reliability, essential for on-device deployment.
Computer Science > Human-Computer Interaction arXiv:2602.23312 (cs) [Submitted on 26 Feb 2026] Title:Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction Authors:Rafael R. Baptista, André de Lima Salgado, Ricardo V. Godoy, Marcelo Becker, Thiago Boaventura, Gustavo J. G. Lahr View a PDF of the paper titled Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction, by Rafael R. Baptista and 5 other authors View PDF HTML (experimental) Abstract:Leader-follower interaction is an important paradigm in human-robot interaction (HRI). Yet, assigning roles in real time remains challenging for resource-constrained mobile and assistive robots. While large language models (LLMs) have shown promise for natural communication, their size and latency limit on-device deployment. Small language models (SLMs) offer a potential alternative, but their effectiveness for role classification in HRI has not been systematically evaluated. In this paper, we present a benchmark of SLMs for leader-follower communication, introducing a novel dataset derived from a published database and augmented with synthetic samples to capture interaction-specific dynamics. We investigate two adaptation strategies: prompt engineering and fine-tuning, studied under zero-shot and one-shot interaction modes, compared with an untrained baseline. Experiments with Qwen2.5-0.5B reveal that zero-shot fine-tuning achieves robust class...
