Primacy Effect of ChatGPT
Yiwei Wang Yujun Cai Muhao Chen Yuxuan Liang Bryan Hooi
Instruction-tuned large language models (LLMs), such as ChatGPT, have led to
promising zero-shot performance in discriminative natural language
understanding (NLU) tasks. This involves querying the LLM using a prompt
containing the question, and the candidate labels to choose from. The
question-answering capabilities of ChatGPT arise from its pre-training on large
amounts of human-written text, as well as its subsequent fine-tuning on human
preferences, which motivates us to ask: Does ChatGPT also inherits humans'
cognitive biases? In this paper, we study the primacy effect of ChatGPT: the
tendency of selecting the labels at earlier positions as the answer. We have
two main findings: i) ChatGPT's decision is sensitive to the order of labels in
the prompt; ii) ChatGPT has a clearly higher chance to select the labels at
earlier positions as the answer. We hope that our experiments and analyses
provide additional insights into building more reliable ChatGPT-based
solutions. We release the source code at
https://github.com/wangywUST/PrimacyEffectGPT.