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@INPROCEEDINGS{Rathkopf:1031878,
author = {Rathkopf, Charles and Millière, Raphaël},
title = {{A}nthropocentric bias and the possibility of artificial
cognition},
reportid = {FZJ-2024-05885},
year = {2024},
abstract = {Evaluating 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 (Type-I), and dismissing LLM mechanistic
strategies that differ from those of humans as not genuinely
competent (Type-II). Mitigating these biases necessitates an
empirically-driven, iterative approach to mapping cognitive
tasks to LLM-specific capacities and mechanisms, which can
be done by supplementing carefully designed behavioral
experiments with mechanistic studies.},
month = {Jul},
date = {2024-07-26},
organization = {International Conference on Machine
Learning, Workshop on LLMs and
Cognition, Vienna (Austria), 26 Jul
2024 - 28 Jul 2024},
subtyp = {Invited},
cin = {INM-7},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5255 - Neuroethics and Ethics of Information (POF4-525)},
pid = {G:(DE-HGF)POF4-5255},
typ = {PUB:(DE-HGF)6},
url = {https://juser.fz-juelich.de/record/1031878},
}