Large Language Models are Biased Because They Are Large Language Models

• URL: http://arxiv.org/abs/2406.13138v1
• Date: Wed, 19 Jun 2024 01:08:03 GMT
• Title: Large Language Models are Biased Because They Are Large Language Models
• Authors: Philip Resnik,
• Abstract summary: This paper seeks to convince the reader that harmful biases are an inevitable consequence arising from the design of large language models. 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.
• Score: 7.156628542829618
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper's primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models. We 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.

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