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001048885 0247_ $$2doi$$a10.1162/COLI.a.582
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001048885 1001_ $$0P:(DE-HGF)0$$aMillière, Raphaël$$b0
001048885 245__ $$aAnthropocentric bias in language model evaluation
001048885 260__ $$aCambridge, MA$$bMIT Press$$c2025
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001048885 520__ $$aEvaluating the cognitive capacities of large language models (LLMs) requires overcoming not only anthropomorphic but also anthropocentric biases. This article identifies two types of anthropocentric bias that have been neglected: overlooking how auxiliary factors can impede LLM performance despite competence (auxiliary oversight), and dismissing LLM mechanistic strategies that differ from those of humans as not genuinely competent (mechanistic chauvinism). Mitigating these biases requires an empirical, iterative approach to mapping cognitive tasks to LLM-specific capacities and mechanisms, achieved by supplementing behavioral experiments with mechanistic studies.
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001048885 7001_ $$0P:(DE-Juel1)176538$$aRathkopf, Charles$$b1$$eCorresponding author$$ufzj
001048885 773__ $$0PERI:(DE-600)2025069-1$$a10.1162/COLI.a.582$$gp. 1 - 10$$p1 - 10$$tComputational linguistics$$v.$$x0891-2017$$y2025
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