Charting the Growth of Social-Physical HRI (spHRI): A Systematic Review Pipeline Augmented by Small Language Models
arXiv:2606.26382v1 Announce Type: cross Abstract: Social-physical human-robot interaction (spHRI) has grown rapidly across robotics, human-computer interaction, human-robot interaction, and haptics. Yet, fragmented terminology and inconsistent methodologies make systematic synthesis difficult. To support scalable review practices, we evaluated the extent to which small language models (SLMs; < 1.5B parameters) can assist with title and abstract screening for a large spHRI systematic review. While no SLMs matched human reviewers' performance, the models operated locally and screened papers orde
The proliferation of language models and increased complexity of interdisciplinary research are driving the need for automated systematic review processes.
This development indicates a pragmatic approach to leveraging AI for scientific knowledge synthesis, particularly in rapidly evolving fields like HRI, addressing challenges of scale and fragmentation.
The research shows that while small language models aren't perfect, they can significantly augment human efforts in literature review, indicating a shift towards AI-assisted scientific methods.
- · Robotics researchers
- · Human-robot interaction field
- · Academic researchers
- · Small language model developers
- · Traditional manual literature review processes
Systematic reviews in complex fields become more frequent and comprehensive due to AI assistance.
The quality and speed of interdisciplinary research synthesis improves, accelerating scientific progress.
AI-augmented discovery processes lead to novel unforeseen connections and breakthroughs across scientific domains.
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Read at arXiv cs.AI