Perspective Transition of Large Language Models for Solving Subjective Tasks
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China（计算机科学与技术系，人工智能研究院，清华大学，北京，中国）；Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China（人工智能产业研究院（AIR），清华大学，北京，中国）；Jiuquan Satellite Launch Center (JSLC), Gansu, China（酒泉卫星发射中心（JSLC），甘肃，中国）；Harbin Institute of Technology, Harbin, China（哈尔滨工业大学，哈尔滨，中国）；College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China（计算机科学与软件工程学院，深圳大学，深圳，中国）；Jiangsu Collaborative Innovation Center for Language Competence, Jiangsu, China（江苏省语言能力协同创新中心，江苏，中国）；Tencent Inc, China（腾讯公司，中国）
Xiaolong Wang, Yuanchi Zhang, Ziyue Wang, Yuzhuang Xu, Fuwen Luo, Yile Wang, Peng Li, Yang Liu
Large language models (LLMs) have revolutionized the field of natural language processing, enabling remarkable progress in various tasks. Different from objective tasks such as commonsense reasoning and arithmetic question-answering, the performance of LLMs on subjective tasks is still limited, where the perspective on the specific problem plays crucial roles for better interpreting the context and giving proper response. For example, in certain scenarios, LLMs may perform better when answering from an expert role perspective, potentially eliciting their relevant domain knowledge. In contrast, in some scenarios, LLMs may provide more accurate responses when answering from a third-person standpoint, enabling a more comprehensive understanding of the problem and potentially mitigating inherent biases. In this paper, we propose Reasoning through Perspective Transition (RPT), a method based on in-context learning that enables LLMs to dynamically select among direct, role, and third-person perspectives for the best way to solve corresponding subjective problem. Through extensive experiments on totally 12 subjective tasks by using both closed-source and open-source LLMs including GPT-4, GPT-3.5, Llama-3, and Qwen-2, our method outperforms widely used single fixed perspective based methods such as chain-of-thought prompting and expert prompting, highlights the intricate ways that LLMs can adapt their perspectives to provide nuanced and contextually appropriate responses for different problems.
Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
University of Oxford（牛津大学）；University of Pennsylvania（宾夕法尼亚大学）；Bocconi University（博科尼大学）；ML Commons；Contextual AI；Factored AI；UCL（伦敦大学学院）；Cohere；MetaAI；New York University（纽约大学）；AWS AI Labs（亚马逊云科技AI实验室）；Meedan
Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；School of Communication Research, University of Amsterdam（阿姆斯特丹大学传播研究学院）；Computer Science Department, University of Turin（都灵大学计算机科学系）
Beyond Majority Voting: Agreement-Based Clustering to Model Annotator Perspectives in Subjective NLP Tasks
Instituto Politécnico Nacional（智利国家理工学院）；University of Wisconsin–Stevens Point（威斯康星州立大学斯蒂文斯点分校）；University of Pretoria（培特里亚大学）；Bahir Dar University（巴希尔达大学）；Imperial College London（伦敦帝国理工学院）；University of Hamburg（汉堡大学）
Simulating multiple human perspectives in socio-ecological systems using large language models
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Highlands Rewilding Limited（高地野化有限公司）；Bayes Business School（贝叶斯商学院）；University of London（伦敦大学）；University of Edinburgh（爱丁堡大学）
Multimodal Mathematical Reasoning with Diverse Solving Perspective
Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Tencent（腾讯）；Center for Future Media, University of Electronic Science and Technology of China（电子科技大学未来媒体中心）
