Large Language Models are Biased Because They Are Large Language Models
This is a foundational critique for AI researchers and practitioners, highlighting a core limitation in current LLM approaches.
The paper argues that harmful biases in large language models are an inevitable consequence of their fundamental design, suggesting that addressing bias requires a fundamental reconsideration of LLM-driven AI.
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.