小型言語モデルによるリーダー・フォロワー役割分類のゼロショット・ワンショット適応評価
Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction
リーダー・フォロワー対話における役割分類のため、小型言語モデル（Qwen2.5-0.5B）をゼロショットとワンショットでファインチューニング・プロンプトエンジニアリングし、精度と遅延を比較評価した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Rafael R. Baptista, André de Lima Salgado, Ricardo V. Godoy, Marcelo Becker, Thiago Boaventura, Gustavo J. G. Lahr
分類: cs.HC, cs.AI, cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
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 classification performance (86.66% accuracy) while maintaining low latency (22.2 ms per sample), significantly outperforming baseline and prompt-engineered approaches. However, results also indicate a performance degradation in one-shot modes, where increased context length challenges the model's architectural capacity. These findings demonstrate that fine-tuned SLMs provide an effective solution for direct role assignment, while highlighting critical trade-offs between dialogue complexity and classification reliability on the edge.
