https://scholars.lib.ntu.edu.tw/handle/123456789/639927
標題: | Evaluating Interfaced LLM Bias | 作者: | Yeh, Kai Ching Chi, Jou An Lian, Da Chen SHU-KAI HSIEH |
關鍵字: | Bias | LangChain | Natural Language Processing | 公開日期: | 1-一月-2023 | 起(迄)頁: | 292 - 299 | 來源出版物: | ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing | 摘要: | In this research, we comprehensively analyze the potential biases inherent in Large Language Model, utilizing meticulously curated input data to ascertain the extent to which such data sway machine-generated responses to yield prejudiced outcomes. Notwithstanding recent strides in mitigating bias in LLM-based NLP, our findings underscore the continued susceptibility of these models to data-driven bias. We have integrated the PTT NTU board as our primary data source for this investigation. Moreover, our study elucidates that, in certain contexts, machines may manifest biases without supplementary prompts. However, they can be guided toward rendering impartial responses when provided with enhanced contextual nuances. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183830386&partnerID=40&md5=319e25e46c5b92a49747cef47e071858 https://scholars.lib.ntu.edu.tw/handle/123456789/639927 |
ISBN: | 9789869576963 |
顯示於: | 語言學研究所 |
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