以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：9

、訪客IP：3.22.120.64

姓名	謝馨頤(Hsin-Yi Hsieh)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	透過臺灣文化視角評估大型語言模型中的社會偏見 (Assessing Social Bias in Large Language Models through a Taiwanese Cultural Lens)
相關論文	★ A Real-time Embedding Increasing for Session-based Recommendation with Graph Neural Networks ★ 基於主診斷的訓練目標修改用於出院病摘之十代國際疾病分類任務 ★ 混合式心臟疾病危險因子與其病程辨識 於電子病歷之研究 ★ 基於 PowerDesigner 規範需求分析產出之快速導入方法 ★ 社群論壇之問題檢索 ★ 非監督式歷史文本事件類型識別──以《明實錄》中之衛所事件為例 ★ 應用自然語言處理技術分析文學小說角色 之關係：以互動視覺化呈現 ★ 基於生醫文本擷取功能性層級之生物學表徵語言敘述：由主成分分析發想之K近鄰算法 ★ 基於分類系統建立文章表示向量應用於跨語言線上百科連結 ★ Code-Mixing Language Model for Sentiment Analysis in Code-Mixing Data ★ 藉由加入多重語音辨識結果來改善對話狀態追蹤 ★ 對話系統應用於中文線上客服助理:以電信領域為例 ★ 應用遞歸神經網路於適當的時機回答問題 ★ 使用多任務學習改善使用者意圖分類 ★ 使用轉移學習來改進針對命名實體音譯的樞軸語言方法 ★ 基於歷史資訊向量與主題專精程度向量應用於尋找社群問答網站中專家
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2026-5-1以後開放)
摘要(中)	隨著大型語言模型 (LLMs) 在各種應用領域廣泛使用，這些模型可能內含的偏見和不公平性問題引起了廣泛關注。現有研究主要集中於評估以英語為主的大型語言模型中存在的偏見，而對非英語語言的偏見評估研究則相對匱乏。以中文為例，簡體中文和繁體中文在語言使用環境、語言特徵及文化內涵上存有差異，現有研究過於集中於簡體中文，忽視了以繁體中文為主的地區語言和文化的獨特性。本研究的主要目標是建立一個細緻的繁體中文偏見基準，用於評估大型語言模型在台灣社會文化背景下的性別和族群偏見。不同於現有研究，我們將深入探討不同人口群體的各種刻板印象的類型，包括性格、制度和文化等方面，以提供更全面的偏見評估視角。我們重新定義了基於CHBias的偏見定義（Bias Specification），並採用困惑度（Perplexity）的平均值作為統計差異度量，以更精確地識別和評估語言模型中的偏見。此外，改善了先前研究直接計算搜集之刻板印象句子的困惑度的方式，我們透過加入提示範本(prompt template) 的方式進行困惑度的計算，試圖在評估模型時以較為符合實際應用的形式進行偏見的評估，降低評估過程高估模型偏見的情況。此外，本研究還將標記句子是否含有有害言論(Toxic Language)，探討其對偏見程度的影響。另外，本研究中提出的評估方法除了用來評出LLMs的偏見之外，亦可用利用評估繼續預訓練模型的偏見，來去窺探訓練數據中存在的偏差。本研究的貢獻在於創建一個基於台灣文化背景的社會偏見 (性別和族群) 基準，可供學術界和產業界使用。通過本研究的方法和發現，我們希望能為評估繁體中文大型語言模型中的社會偏見提供有價值的參考，促進大型語言模型朝向更公平的方向前進，以便更好地服務於多元化的應用場景之中。
摘要(英)	As Large Language Models (LLMs) are widely used in various applications, the potential biases and unfairness embedded in these models have received considerable attention. Existing studies have primarily focused on assessing bias in English LLMs, while research on bias assessment for non-English languages remains relatively scarce. Taking Chinese as an example, Simplified Chinese and Traditional Chinese differ in linguistic environments, language features, and cultural connotations, yet existing research has been overly concentrated on Simplified Chinese, neglecting the unique linguistic and cultural characteristics of regions where Traditional Chinese is the predominant form. The primary objective of this study is to establish a Traditional Chinese social bias benchmark for evaluating the handling of gender and ethnic group bias in large language models in the context of Taiwanese cultural background. Unlike previous research, we delve deeper into the various types of stereotypes in different demographic groups, including personality, institutional, cultural aspects, etc., to provide a more comprehensive bias assessment perspective. We redefine the Bias Specification based on CHBias and adopt the average perplexity as the metric for statistical difference calculation to more accurately identify and evaluate the biases present in language models. Furthermore, this study has improved upon previous research that directly calculated the perplexity (PPL) of collected stereotypical sentences by incorporating prompt templates for PPL calculation. This approach attempts to evaluate biases in a manner closer to actual application scenarios and reduces the overestimation of model biases during the evaluation process. Additionally, this study annotates whether sentences contain harmful speech and explores its impact on the degree of bias. Moreover, the proposed evaluation method can also be used to analyze biases in the training data by evaluating continual pretraining models and inferring biases present in the training data. The contributions of this research lie in the creation of a social bias(gender and ethnic group) benchmark based on the Taiwanese cultural context, which can be used by both academia and industry. Through the methods and findings of this study, we hope to provide valuable references for assessing social biases in Traditional Chinese large language models, promoting fairness and reducing bias in these models to better serve diverse application scenarios.
關鍵字(中)	★ 大型語言模型 ★ 人工智慧公平性 ★ 語言模型公平性 ★ 臺灣社會 ★ 社會偏見評估 ★ 社會偏見 ★ 偏見評估 ★ 刻板印象 ★ 平均困惑度 ★ 提示範本 ★ 訓練數據偏見	關鍵字(英)	★ Large Language Models ★ LLM ★ AI Fairness ★ Fairness in Language Models ★ Taiwanese Society ★ Social Bias ★ Social Bias Evaluation ★ Bias Evaluation ★ Stereotypes ★ Average Perplexity ★ PPL ★ Prompt Template ★ Training Data Bias
論文目次	中文摘要 i Abstract iii 誌謝 v Contents vii List of Figures x List of Tables xii 1 Introduction 1 1.1 Motivation 1 1.2 Research Goal 3 2 Related Works 4 3 Data Collection 8 3.1 Bias Specification 8 3.2 Data Collection 10 3.2.1 Data Sources 10 3.2.2 Bias and Toxicity Annotation 11 3.2.3 Attribute Category 13 3.3 Data Quality 18 3.3.1 Inter-Annotation Agreement 18 3.3.2 Quality Review Question 19 4 Social Bias Evaluation Framework 21 4.1 Bias Measurement 21 4.2 Perplexity Calculation with Prompt Templates . 23 5 Experiment and Results 26 5.1 Necessity of Adding Prompt Templates 26 5.1.1 Implementation Details 26 5.1.2 Results 28 5.2 Social Bias in Traditional Chinese LLMs 29 5.2.1 Results 29 6 Ablation Study 38 6.1 The Impact of Toxic Language in Bias Assessment 38 6.2 The Impact of Training Data on Model Bias 39 6.2.1 Implementation Details 40 6.2.2 Results 41 7 Conclusion 44 8 Ethical Statement and Limitations 46 A Bias Specification 48 A.1 Target Term 48 A.1.1 Gender 48 A.1.2 Taiwanese Ethnic Groups 49 A.2 Attribute Term 49 A.3 Attribute Category Description 53 B Annotation Guideline 55 B.1 Bias Sentence 55 C YouTube Video List 56 Bibliography 77
參考文獻	[1] 蕭宇, “台男悲歌？「母豬教」論述中的陽剛焦慮與厭女邏輯,” Master’s thesis, 高雄醫學大學, Jan 2021. [2] Y. Li, M. Du, R. Song, X. Wang, and Y. Wang, “A survey on fairness in large language models,” 2024. [3] Y. Chang, X. Wang, J. Wang, Y. Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y. Wang et al., “A survey on evaluation of large language models,” ACM Transactions on Intelligent Systems and Technology. [4] I. O. Gallegos, R. A. Rossi, J. Barrow, M. M. Tanjim, S. Kim, F. Dernoncourt, T. Yu, R. Zhang, and N. K. Ahmed, “Bias and fairness in large language models: A survey,” 2024. [5] S. Barikeri, A. Lauscher, I. Vulić, and G. Glavaš, “RedditBias: A real-world resource for bias evaluation and debiasing of conversational language models,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), C. Zong, F. Xia, W. Li, and R. Navigli, Eds. Online: Association for Computational Linguistics, Aug. 2021, pp. 1941–1955. [Online]. Available: https://aclanthology.org/2021.acl-long.151 [6] J. Zhao, M. Fang, Z. Shi, Y. Li, L. Chen, and M. Pechenizkiy, “CHBias: Bias evaluation and mitigation of Chinese conversational language models,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 13 538–13 556. [Online]. Available: https://aclanthology.org/2023.acl-long.757 [7] W. Zhong, R. Cui, Y. Guo, Y. Liang, S. Lu, Y. Wang, A. Saied, W. Chen, and N. Duan, “Agieval: A human-centric benchmark for evaluating foundation models,” 2023. [8] H. Zeng, “Measuring massive multitask chinese understanding,” 2023. [9] Y. Huang, Y. Bai, Z. Zhu, J. Zhang, J. Zhang, T. Su, J. Liu, C. Lv, Y. Zhang, J. Lei, Y. Fu, M. Sun, and J. He, “C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models,” 2023. [10] H. Li, Y. Zhang, F. Koto, Y. Yang, H. Zhao, Y. Gong, N. Duan, and T. Baldwin, “Cmmlu: Measuring massive multitask language understanding in chinese,” 2023. [11] X. Zhang, C. Li, Y. Zong, Z. Ying, L. He, and X. Qiu, “Evaluating the performance of large language models on gaokao benchmark,” 2023. [12] L. Xu, H. Hu, X. Zhang, L. Li, C. Cao, Y. Li, Y. Xu, K. Sun, D. Yu, C. Yu, Y. Tian, Q. Dong, W. Liu, B. Shi, Y. Cui, J. Li, J. Zeng, R. Wang, W. Xie, Y. Li, Y. Patterson, Z. Tian, Y. Zhang, H. Zhou, S. Liu, Z. Zhao, Q. Zhao, C. Yue, X. Zhang, Z. Yang, K. Richardson, and Z. Lan, “CLUE: A Chinese language understanding evaluation benchmark,” in Proceedings of the 28th International Conference on Computational Linguistics, D. Scott, N. Bel, and C. Zong, Eds. Barcelona, Spain (Online): International Committee on Computational Linguistics, Dec. 2020, pp. 4762–4772. [Online]. Available: https://aclanthology.org/2020.coling-main.419 [13] L. Xu, A. Li, L. Zhu, H. Xue, C. Zhu, K. Zhao, H. He, X. Zhang, Q. Kang, and Z. Lan, “Superclue: A comprehensive chinese large language model benchmark,” 2023. [14] Z. Gu, X. Zhu, H. Ye, L. Zhang, J. Wang, Y. Zhu, S. Jiang, Z. Xiong, Z. Li, W. Wu, Q. He, R. Xu, W. Huang, J. Liu, Z. Wang, S. Wang, W. Zheng, H. Feng, and Y. Xiao, “Xiezhi: An ever-updating benchmark for holistic domain knowledge evaluation,” 2024. [15] C. C. Shao, T. Liu, Y. Lai, Y. Tseng, and S. Tsai, “Drcd: a chinese machine reading comprehension dataset,” 2019. [16] P. Ennen, P.-C. Hsu, C.-J. Hsu, C.-L. Liu, Y.-C. Wu, Y.-H. Liao, C.-T. Lin, D.-S. Shiu, and W.-Y. Ma, “Extending the pre-training of bloom for improved support of traditional chinese: Models, methods and results,” 2023. [17] S.-B. Luo, C.-C. Fan, K.-Y. Chen, Y. Tsao, H.-M. Wang, and K.-Y. Su, “Chinese movie dialogue question answering dataset,” in Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), Y.-C. Chang and Y.-C. Huang, Eds. Taipei, Taiwan: The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), Nov. 2022, pp. 7–14. [Online]. Available: https://aclanthology.org/2022.rocling-1.2 [18] C.-J. Hsu, C.-L. Liu, F.-T. Liao, P.-C. Hsu, Y.-C. Chen, and D. shan Shiu, “Advancing the evaluation of traditional chinese language models: Towards a comprehensive benchmark suite,” 2023. [19] Z.-R. Tam, Y.-T. Pai, Y.-W. Lee, S. Cheng, and H.-H. Shuai, “An improved traditional chinese evaluation suite for foundation model,” arXiv preprint arXiv:2403.01858, 2024. [20] Y. Liu, Y. Yao, J.-F. Ton, X. Zhang, R. Guo, H. Cheng, Y. Klochkov, M. F. Taufiq, and H. Li, “Trustworthy llms: a survey and guideline for evaluating large language models’ alignment,” 2024. [21] L. Sun, Y. Huang, H. Wang, S. Wu, Q. Zhang, Y. Li, C. Gao, Y. Huang, W. Lyu, Y. Zhang, X. Li, Z. Liu, Y. Liu, Y. Wang, Z. Zhang, B. Vidgen, B. Kailkhura, C. Xiong, C. Xiao, C. Li, E. Xing, F. Huang, H. Liu, H. Ji, H. Wang, H. Zhang, H. Yao, M. Kellis, M. Zitnik, M. Jiang, M. Bansal, J. Zou, J. Pei, J. Liu, J. Gao, J. Han, J. Zhao, J. Tang, J. Wang, J. Vanschoren, J. Mitchell, K. Shu, K. Xu, K.-W. Chang, L. He, L. Huang, M. Backes, N. Z. Gong, P. S. Yu, P.-Y. Chen, Q. Gu, R. Xu, R. Ying, S. Ji, S. Jana, T. Chen, T. Liu, T. Zhou, W. Wang, X. Li, X. Zhang, X. Wang, X. Xie, X. Chen, X. Wang, Y. Liu, Y. Ye, Y. Cao, Y. Chen, and Y. Zhao, “Trustllm: Trustworthiness in large language models,” 2024. [22] T. Hagendorff, “Mapping the ethics of generative ai: A comprehensive scoping review,” 2024. [23] H. Sun, Z. Zhang, J. Deng, J. Cheng, and M. Huang, “Safety assessment of chinese large language models,” 2023. [24] G. Xu, J. Liu, M. Yan, H. Xu, J. Si, Z. Zhou, P. Yi, X. Gao, J. Sang, R. Zhang, J. Zhang, C. Peng, F. Huang, and J. Zhou, “Cvalues: Measuring the values of chinese large language models from safety to responsibility,” 2023. [25] Z. Zhang, L. Lei, L. Wu, R. Sun, Y. Huang, C. Long, X. Liu, X. Lei, J. Tang, and M. Huang, “Safetybench: Evaluating the safety of large language models with multiple choice questions,” arXiv preprint arXiv: 2309.07045, 2023. [26] W. Wang, Z. Tu, C. Chen, Y. Yuan, J. tse Huang, W. Jiao, and M. R. Lyu, “All languages matter: On the multilingual safety of large language models,” 2023. [27] L. Xu, K. Zhao, L. Zhu, and H. Xue, “Sc-safety: A multi-round open-ended question adversarial safety benchmark for large language models in chinese,” 2023. [28] K. Huang, X. Liu, Q. Guo, T. Sun, J. Sun, Y. Wang, Z. Zhou, Y. Wang, Y. Teng, X. Qiu, Y. Wang, and D. Lin, “Flames: Benchmarking value alignment of chinese large language models,” 2023. [29] Y. Huang and D. Xiong, “Cbbq: A chinese bias benchmark dataset curated with human-ai collaboration for large language models,” 2023. [30] 王甫昌, “台灣弱勢族群意識發展之歷史過程考察,” 台灣文學研究, no. 4, pp. 60–83, Jun 2013. [31] 謝國斌, “台灣族群研究的發展,” 台灣原住民族研究學報, vol. 1, no. 1, pp. 1–27, Mar 2011. [32] A. Caliskan, J. J. Bryson, and A. Narayanan, “Semantics derived automatically from language corpora contain human-like biases,” Science, vol. 356, no. 6334, p. 183–186, Apr. 2017. [Online]. Available: http://dx.doi.org/10.1126/science.aal4230 [33] A. Lauscher, G. Glavaš, S. P. Ponzetto, and I. Vulić, “A general framework for implicit and explicit debiasing of distributional word vector spaces,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 05, pp. 8131–8138, Apr. 2020. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/6325 [34] 鍾佳玲, “族群通婚中的性別文化與權力配置,” Master’s thesis, 國立中央大學, Jan 2007. [35] 王雯君, “婚姻對女性族群認同的影響－以台灣閩客通婚為例,” 思與言：人文 與社會科學期刊, vol. 43, no. 2, pp. 119–178, Jun 2005. [36] 徐振傑, “女性商品，男性代言電視廣告中的“新＂男性形象與再現意涵,” 傳 播與管理研究, vol. 3, no. 2, pp. 133–159, Jan 2004. [37] 胡瓊勻, “汽車廣告所再現的「新女性」形象,” 文化事業與管理研究, vol. 23, no. 2, pp. 28–46, Apr 2023. [38] 林吟鴻, “臺灣民眾性別角色態度之研究,” Master’s thesis, 國立臺灣大學, Jan 2011. [39] 朱蘭慧, “男性性別角色刻板印象之形成與鬆動,” 應用心理研究, no. 17, pp.85–119, Mar 2003. [40] 賴禹安, “受害不失男子氣概？遭偷拍男性的受害經驗初探,” Master’s thesis, 國 立政治大學, Jan 2021. [41] 蔡沛毓, “不同世代男性在婚姻關係中的性別角色展演,” Master’s thesis, 台南應 用科技大學, Jan 2013. [42] 高穎超, “做兵、 儀式、 男人類：台灣義務役男服役過程之陽剛氣質研究 （2000-2006）,” Master’s thesis, 國立臺灣大學, Jan 2006. [43] 孫子靖 and 呂明蓁, “「厭娘」與「拒 c」？—大專校院學生性別刻板印象之探 究,” 性別平等教育季刊, no. 96, pp. 153–156, Jan 2022. [44] 林慧慈, “從粉色浪潮談刻板印象、偏見與歧視,” 清流雙月刊, no. 28, pp. 35–39, Jul 2020. [45] 郭爵菀, “男性科技工程師婚姻觀之研究－男性研究的理論觀點,” Master’s thesis, 國立暨南國際大學, Jan 2009. [46] 黃淑玲, “男子性與喝花酒文化：以 bourdieu 的性別支配理論為分析架構,” 台灣社會學, no. 5, pp. 73–132, Jun 2003. [47] 台灣客家研究概論:. 行政院客家委員會, 2007. [Online]. Available: https://books.google.com.tw/books?id=_aVThrwC7lUC [48] 蔡芬芳, “性別、 族群與客家研究,” 女學學誌：婦女與性別研究, no. 39, pp.165–203, Dec 2016. [49] 馮建彰, “臺鐵客家人的工作與生活,” 客．觀, no. 4, pp. 35–57, Aug 2023. [50] 王雯君, “客家邊界：客家意象的詮釋與重建,” 東吳社會學報, no. 18, pp.117–156, Jun 2005. [51] 孫榮光, “電視綜藝節目的象徵暴力與客籍藝人的生存心態：以小鐘、澎澎為 例,” 人文社會科學研究, vol. 10, no. 4, pp. 23–43, Dec 2016. [52] 莊雅涵, 王奕婷, and 吳偉立, “我們活在不同的台北？－台北政治暨文化性格： 1994-2002,” 政治科學論叢, no. 21, pp. 49–74, Sep 2004. [53] 譚光鼎, “被扭曲的他者：教科書中原住民偏見的檢討,” 課程與教學, vol. 11, no. 4, pp. 27–49, Nov 2008. [54] 呂枝益, “教科書中族群偏見的探討與革新,” 原住民教育季刊, no. 17, pp. 34–51, Feb 2000. [55] 蘇船利, “當原住民學生遇到漢族老師,” 師友月刊, no. 468, pp. 40–43, Jun 2006. [56] 童一寧, “外省第三代的國家認同,” Master’s thesis, 國立臺灣大學, Jan 2005. [57] 張寧珈, “感性與理性：外省族群意識、民族認同與國家想像,” Master’s thesis, 國立臺灣大學, Jan 2018. [58] 吳永毅, “香蕉． 豬公． 國：「返鄉」電影中的外省人國家認同,” 中外文學, vol. 22, no. 1, pp. 32–44, Jun 1993. [59] 陳師孟, “外省族群與統獨迷思,” 新使者, no. 50, pp. 21–24, Feb 1999. [60] J. Cohen, “A coefficient of agreement for nominal scales,” Educational and Psychological Measurement, vol. 20, no. 1, pp. 37–46, 1960. [Online]. Available: https://doi.org/10.1177/001316446002000104 [61] D. Colquhoun, “The reproducibility of research and the misinterpretation of p values,” Royal Society Open Science, vol. 4, p. 171085, 12 2017. [62] H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom, “Llama 2: Open foundation and fine-tuned chat models,” 2023. [63] A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M.-A. Lachaux, P. Stock, T. L. Scao, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed, “Mistral 7b,” 2023. [64] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. F. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” in Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35. Curran Associates, Inc., 2022, pp. 27 730–27 744. [Online]. Available: https://proceedings.neurips.cc/paper_files/ paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf [65] Y.-T. Lin and Y.-N. Chen, “Taiwan llm: Bridging the linguistic divide with a culturally aligned language model,” 2023. [66] C.-J. Hsu, C.-L. Liu, F.-T. Liao, P.-C. Hsu, Y.-C. Chen, and D.-S. Shiu, “Breeze-7b technical report,” 2024. [67] TAIDE, “Taide-lx-7b-chat,” 2024. [Online]. Available: https://huggingface.co/taide/TAIDE-LX-7B-Chat [68] L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing et al., “Judging llm-as-a-judge with mt-bench and chatbot arena,” Advances in Neural Information Processing Systems, vol. 36, 2024. [69] E. Ferrara, “Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies,” Sci, vol. 6, no. 1, p. 3, Dec. 2023. [Online]. Available: http://dx.doi.org/10.3390/sci6010003 [70] J. Giner-Miguelez, A. Gómez, and J. Cabot, “Describeml: a tool for describing machine learning datasets,” in Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, ser. MODELS ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 22–26. [Online]. Available: https://doi.org/10.1145/3550356.3559087 [71] A. Yohannis and D. Kolovos, “Towards model-based bias mitigation in machine learning,” in Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems, ser. MODELS ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 143–153. [Online]. Available: https://doi.org/10.1145/3550355.3552401 [72] K. Webster, X. Wang, I. Tenney, A. Beutel, E. Pitler, E. Pavlick, J. Chen, and S. Petrov, “Measuring and reducing gendered correlations in pre-trained models,” CoRR, vol. abs/2010.06032, 2020. [Online]. Available: https://arxiv.org/abs/2010.06032 [73] M. Nadeem, A. Bethke, and S. Reddy, “StereoSet: Measuring stereotypical bias in pretrained language models,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), C. Zong, F. Xia, W. Li, and R. Navigli, Eds. Online: Association for Computational Linguistics, Aug. 2021, pp. 5356–5371. [Online]. Available: https://aclanthology.org/2021.acl-long.416 [74] Y. Cui, Z. Yang, and X. Yao, “Efficient and effective text encoding for chinese llama and alpaca,” 2023. [75] I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations, 2019. [Online]. Available: https://openreview.net/forum?id=Bkg6RiCqY7
指導教授	蔡宗翰(Tzong-Han Tsai)	審核日期	2024-5-2
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare