How Should I Pick a Foundation Model for My Robot? In Favor of a Community Evaluation Framework for Social Robots
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Abstract (arXiv)
Researchers who seek to build social robot applications on foundation models are faced with a difficult question: how should we pick a model? Public leaderboards offer little guidance: the demands of real-time, embodied social interaction lie largely outside their focus. And direct evaluation is impractical at scale: each embodied study requires scarce participant, robot, and experimenter time. In this paper, we identify five evaluation dimensions for foundation models in social robots: (i) conversational competence, (ii) user safety, (iii) embodied character, (iv) target scene effectiveness, and (v) audience appropriateness. To make model selection cheaper and better informed, we propose a three-tiered evaluation funnel paradigm that first filters with general metrics, then extends to simulated interactions, and terminates in more expensive, robot-specific evaluation. We map all five dimensions across all three tiers, chart where applicable evaluation methods exist and are missing, and close with a call to action: let's build the evaluation framework together as a community.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 35 references that are in the map. First posted on arXiv 7 Aug 2026.
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23 of 35 references.
References · 23
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2020Measuring Massive Multitask Language Understandingcited by 7,963
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
- 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
- 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
- 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
- 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
- 2024A Survey on LLM-as-a-Judgecited by 1,423
- 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
- 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
- 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
- 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 505
11 more
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- 2024LiveBench: A Challenging, Contamination-Limited LLM Benchmarkcited by 262
- 2024OR-Bench: An Over-Refusal Benchmark for Large Language Modelscited by 244
- 2024BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practicescited by 114
- 2024SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agentscited by 93
- 2025RoboArena: Distributed Real-World Evaluation of Generalist Robot Policiescited by 64
- 2024Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMscited by 59
- 2025EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safetycited by 21
- 2024Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Harucited by 5
- 2024Towards Pareto Optimal Throughput in Small Language Model Servingcited by 5
- 2025Robot-Led Vision Language Model Wellbeing Assessment of Childrencited by 4
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