Charting the Growth of Social-Physical HRI (spHRI): A Systematic Review Pipeline Augmented by Small Language Models
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Computer Science > Computation and Language
Title:Charting the Growth of Social-Physical HRI (spHRI): A Systematic Review Pipeline Augmented by Small Language Models
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 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.
| Comments: | 5 pages, 3 figures, 2 tables, Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Human-Computer Interaction (cs.HC); Robotics (cs.RO) |
| Cite as: | arXiv:2606.26382 [cs.CL] |
| (or arXiv:2606.26382v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26382
arXiv-issued DOI via DataCite (pending registration)
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| Related DOI: | https://doi.org/10.1145/3776734.3794506
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