中國計算機學會通訊2024年第11期專題聚焦“大模型時代下的人機交互”,，深度剖析了這一時代下人機交互領域的最新研究成果與前沿趨勢。該專題邀請了該領域的杰出學者,，精心撰寫了五篇學術(shù)力作,，全方位多維度探討了大模型對人機交互的深遠影響與促進作用。《以人為中心的大模型Agent社會交互模擬——以推薦系統(tǒng)為例》是其中一個關鍵議題,，它旨在分析和理解用戶與大模型Agent之間的復雜交互關系,，構(gòu)建與人類價值對齊、公平無偏,、可解釋,、可信可靠的大模型Agent模擬器，并著重探討這一模擬器如何解釋和評估其對用戶期望價值目標的影響,、塑造以及潛在風險,。這一前沿探索不僅為理解用戶與大模型Agent之間的交互機制提供了新的視角和方法，更為構(gòu)建更加健康,、公平且可持續(xù)的數(shù)字社會環(huán)境提供了有力支持，引領大模型智能體技術(shù)邁向真正以人為本的新篇章,。
作者信息
如果您對本文內(nèi)容感興趣的話,，可以與作者聯(lián)系：
張鵬 復旦大學計算機科學技術(shù)學院副教授、碩士生導師
聯(lián)系方式：[email protected]
實驗室相關論文
[1] Guangping Zhang, Dongsheng Li, Hansu Gu, Tun Lu, Li Shang, Ning Gu. Simulating News Recommendation Ecosystems for Insights and Implications. In IEEE Transactions on Computational Social Systems, vol. 11, no. 5, pp. 5699-5713, Oct. 2024.
[2] Yubo Shu, Haonan Zhang, Hansu Gu, Peng Zhang, Tun Lu, Dongsheng Li, Ning Gu. RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents. In IEEE Transactions on Computational Social Systems, vol. 11, no. 5, pp. 6759-6770, Oct. 2024.
[3] Yaqiong Li, Peng Zhang, Hansu Gu, Tun Lu, Siyuan Qiao, Yubo Shu, Yiyang Shao, Ning Gu. DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship. In Proceedings of the ACM on Human-Computer Interaction, 2025.
[4] Shitong Duan, Xiaoyuan Yi, Peng Zhang, Tun Lu, Xing Xie, and Ning Gu. 2024. Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning. In the Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024.
[5] Shitong Duan, Xiaoyuan Yi, Peng Zhang, Tun Lu, Xing Xie, and Ning Gu. 2024. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, November 12-16, 2024.
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