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언어모델의 편향 개선을 위한 프롬프트 엔지니어링 연구 : ChatGPT를 활용한 정치인 감성분석 말뭉치를 중심으로 ×
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CORPUS LINGUSITICS RESEARCH Vol.8 No.1 pp.49-66
언어모델의 편향 개선을 위한 프롬프트 엔지니어링 연구 : ChatGPT를 활용한 정치인 감성분석 말뭉치를 중심으로
Key Words : Political Bias,ChatGPT,Prompt Engineering,Debiasing,LLM
Abstract
This study aims to identify and address the inherent political bias in ChatGPT by utilizing a sentiment analysis task. We set up representative figures from each political faction, asked chatgpt to write about the politicians using various prompts, and then sentimentally analyzed their outputs to determine the bias of ChatGPT. We found that ChatGPT is more positively biased toward liberal politicians in South Korea. We also found that ChatGPT's bias can be reduced by combining general narratives that encourage neutral writing or by refining the prompts with variables such as tone and writing style. This study provides important insights into the responsible use of AI and how to improve its bias.