Large Language Models are Biased Because They Are Large Language Models

2024-06-19

arXiv.org

AI Summary

This paper investigates the structural origins of harmful biases in large language models (LLMs), arguing that such biases are not artifacts of data noise or training errors, but rather inherent deficiencies arising from the coupling of scale expansion, the statistical nature of language modeling, and the data-driven paradigm. Method: Through rigorous theoretical analysis, derivation from first principles of language modeling, and critical reflection on mechanisms of socio-semantic embedding, the authors systematically demonstrate—novelly—that bias is fundamentally irreducible: it cannot be fully eliminated via post-hoc mitigation, dataset curation, or alignment fine-tuning. Contribution/Results: The core contribution is the original thesis that “bias is an architectural problem,” necessitating a fundamental reexamination of LLM design assumptions. This reframes AI ethics discourse away from incremental technical fixes toward foundational paradigm shifts in model architecture and learning principles.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Examines bias in large language models (LLMs).
• Links harmful biases to LLM design principles.
• Calls for rethinking AI foundations to address bias.

Innovation

• Examines bias in large language models
• Links bias to LLM design fundamentals
• Calls for rethinking AI foundational assumptions