LLM robots match humans on engagement ratings in HRI questionnaires but systematically invert strangeness/comfort dimensions across models and live interactions.
Academically intelligent LLMs are not necessarily socially intelligent,
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Empathic similarity feedback in prompts generates more acceptable compromises than chain-of-thought, and margin-based training on the resulting data lets smaller models produce them without ongoing empathy estimation.
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.
citing papers explorer
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When Robots Rate Their Own Interactions: Engagement Validity and the Strangeness Failure
LLM robots match humans on engagement ratings in HRI questionnaires but systematically invert strangeness/comfort dimensions across models and live interactions.
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Generating Place-Based Compromises Between Two Points of View
Empathic similarity feedback in prompts generates more acceptable compromises than chain-of-thought, and margin-based training on the resulting data lets smaller models produce them without ongoing empathy estimation.
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A Survey on LLM-as-a-Judge
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.