Benchmarking Local Language Models for Social Robots using Edge Devices
Social-educational robots designed for socially interactive pedagogical support, such as the Robot Study Companion (RSC), rely on responsive, privacy-preserving interaction despite severely limited compute. However, there is a gap in systematic benchmarking of language models for edge computing in pedagogical applications. This paper benchmarks 25 open-source language models for local deployment on edge hardware. We evaluate each model across three dimensions: inference efficiency (tokens per second, energy consumption), general knowledge (a six-category MMLU subset), and teaching effectiveness (LLM-rated pedagogical quality), validated against five independent human raters using the Rasp...
| Authors | Dorian Lamouille, Matevž B. Zorec, Farnaz Baksh et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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