Introduction

As AI models like ChatGPT take on a growing role in judging which answers qualify as "correct," they must remain impartial. Yet large language models (LLMs) used in these roles often display disproportionate self-preference, selecting their own outputs even when stronger, more accurate alternatives exist.

This becomes especially concerning given that researchers estimate that 30-40% of all online text, the very material on which many models are trained on, is already AI-generated. If models both produce a large share of content and consistently prefer their own ideas, they can end up reinforcing those ideas over time. This risks amplifying repetition; filtering out human input; and influencing what millions see, believe, and act upon without awareness. As AI becomes more embedded in search, writing tools, and information platforms, this bias can quietly distort our shared understanding.

In our research, we confirmed that Meta's Llama-3.1-8B-Instruct shows this dangerous tendency, and we built steering vectors that allow us to enhance or suppress the bias in a controlled way. We found that applying this tool was very effective, allowing us to essentially change self-preference at will. We also observed a similar propensity in DeepSeek-V3, but did not have sufficient compute to explore further.

This post walks through how we detected self-preference bias, how the steering method works, and why managing self-preference is necessary as AI systems play a growing role in decision making.

Background

Defining Self-Evaluation and Self-Preference

Recently, researchers have begun to use LLMs themselves to grade outputs given a prompt. If a model prefers its own output above any other answer, we say it exhibits self-preference. A slight preference is tolerable if the chosen answer is genuinely better, yet a persistent bias - termed disproportionate self-preference - allows weaker ideas to pass simply because they come from the same model.^[1]

This self-preference phenomenon has been widely observed in LLMs. For example, GPT-4 consistently rates its own outputs higher than rival models. Similarly, studies show that self-favoritism appears in text-summarization tasks where human judges see no quality gap. Follow-up studies reveal another phenomenon; judge models can reliably recognize their own writing, and the better they are at spotting themselves, the stronger thei...

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