Work

Primacy Effect of ChatGPT

Year: 2023

Type: conference-paper

Abstract: 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 inherit 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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2023.emnlp-main.8

Authors: Yiwei Wang, Yujun Cai, Muhao Chen, Yuxuan Liang, Bryan Hooi

Institutions: University of California, Los Angeles, University of California, Davis, The Hong Kong University of Science and Technology (Guangzhou), National University of Singapore

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid