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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},
}