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Large Language Models are Biased Because They Are Large Language Models
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This position paper's primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models. I do this by seeking to convince the reader that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.
Forward citations
Cited by 2 Pith papers
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BiasLab: A Multilingual Dual-Framing Framework for LLM Bias Measurement, Applied to Workplace and HR Contexts
BiasLab uses mirrored affirmative/reverse prompt pairs across 12 languages to quantify directional preferences in 10 LLMs, claiming a systematic asymmetry between rejection and endorsement.
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Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons
A knockout tournament of iterative pairwise LLM comparisons improves agreement with human expert scores by 0.07 Pearson on average across exam grading and MT evaluation.
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