This research explores how human-defined goals influence the behavior of Large Language Models (LLMs) through purpose-conditioned cognition. Using financial prediction tasks, we show that revealing the downstream use (e.g., predicting stock returns or earnings) of LLM outputs leads the LLM to generate biased sentiment and competition measures, even though these measures are intended to be downstream task-independent. Goal-aware prompting shifts intermediate measures toward the disclosed downstream objective. This purpose leakage improves performance before the LLM's knowledge cutoff, but with no advantage post-cutoff. AI bias due to "seeing the goal" is not an algorithmic flaw, but stems from human accountability in research design to ensure the statistical validity and reliability of AI-generated measurements.

翻译：本研究探讨了通过目标条件认知，人类定义的目标如何影响大型语言模型（LLM）的行为。利用金融预测任务，我们表明，揭示LLM输出的下游用途（例如预测股票收益或盈利）会导致LLM生成带有偏见的情感和竞争度量，尽管这些度量本意应独立于下游任务。目标感知提示使中间度量向已披露的下游目标偏移。这种目标泄露在LLM的知识截止日期前提升了性能，但在截止日期后并无优势。因“看见目标”导致的AI偏见并非算法缺陷，而是源于研究设计中为确保AI生成度量的统计有效性和可靠性所需的人类责任。