Charting the Growth of Social-Physical HRI (spHRI): A Systematic Review Pipeline Augmented by Small Language Models
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Abstract (arXiv)
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 orders of magnitude faster. The combined SLM ensemble identified 39 papers reviewers missed, representing 10.29% of the final relevant dataset. These results demonstrate that SLMs can augment, rather than replace, expert reviewers and make large-scale literature reviews accessible and sustainable.
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References · 5
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
- 2023Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languagescited by 36
- 2023Appraising the Potential Uses and Harms of LLMs for Medical Systematic Reviewscited by 18
- 2024Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devicescited by 17
- 2024Touch in Human Social Robot Interaction: Systematic Literature Review with PRISMA Methodcited by 2
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