Centralized matching mechanisms outperform free negotiation in stability and efficiency with LLM agents, who also report preferences truthfully more often than humans, though not always in line with strategy-proofness predictions.
Kirshner, Anton Ovchinnikov, Meena Andiappan, and Tracy Jenkin
2 Pith papers cite this work, alongside 43 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
STAMP-LLM is a two-phase psychometric protocol for designing and applying bias measures to LLMs, illustrated with one explicit and two implicit racial bias tests.
citing papers explorer
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Do Matching Mechanisms Work with LLM Agents?
Centralized matching mechanisms outperform free negotiation in stability and efficiency with LLM agents, who also report preferences truthfully more often than humans, though not always in line with strategy-proofness predictions.
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Designing Psychometric Bias Measures for ChatBots: An Application to Racial Bias Measurement
STAMP-LLM is a two-phase psychometric protocol for designing and applying bias measures to LLMs, illustrated with one explicit and two implicit racial bias tests.