Anthropocentric bias and the possibility of artificial cognition
<|reference_start|>Anthropocentric bias and the possibility of artificial cognition: 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.<|reference_end|>
@article{millière2024anthropocentric,
    title={Anthropocentric bias and the possibility of artificial cognition},
    author={Rapha"el Milli`ere, Charles Rathkopf},
    journal={arXiv preprint arXiv:2407.03859},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03859},
    primaryClass={cs.CL}
}