知能の収穫逓減：短期間のオープンエンドな社会的ヒューマンロボットインタラクションにおけるLLM規模とユーザー知覚の非線形関係
Diminishing Returns of Intelligence: The Non-Linear Relationship Between LLM Scale and User Perception in Short-Duration Open-Ended Social Human-Robot Interactions
19人の参加者が4B、8B、30BパラメータのLLMを搭載したロボットと短時間の自由会話を行い、知覚される知能や自然さに有意差がないことを示し、モデル規模の拡大が短時間の社会的インタラクションでは限定的な効果しかないことを明らかにした。
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著者: Amanda Rasille Røn Volf, Morten Roed Frederiksen
分類: cs.RO
原文アブストラクト
Large Language Models (LLMs) are increasingly used to drive embodied social agents, yet it remains unclear whether larger models improve user perception during brief human-robot encounters. This paper examines the effect of LLM parameter size on short-duration, open-ended social interactions with a robot interface. In a within-subjects study, 19 participants interacted with robot faces driven by Qwen3-VL models at 4B, 8B, and 30B parameters. Participants evaluated the interactions in terms of perceived intelligence, naturalness, enjoyment, and humor. Results showed no significant overall preference for the 30B model over the smaller variants, including no significant advantage over the 4B model in perceived naturalness or intelligence. A significant relationship between AI interaction frequency and intelligence rankings for the 30B model suggests that more experienced users may be more sensitive to differences in model capability. Overall, the findings indicate diminishing returns from model scaling in brief open-ended social HRI, where conversational flow, responsiveness, and socially appropriate behavior potentially matter as much as raw parameter count.