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arXiv 2309.02045cs.CLcs.AIcs.LG
通过提示策略增强评论文本的多领域情感分析
Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies
Hong Kong Baptist University（香港浸会大学）；BNU-UIC Institute of AI and Future Networks（北师大-联合国际学院人工智能与未来网络研究院）；BNU-HKBU United International College（北师大-港浸大联合国际学院）；Beijing Normal University（北京师范大学）
Large Language Models (LLMs) have made significant strides in both scientific research and practical applications. Existing studies have demonstrated the state-of-the-art (SOTA) performance of LLMs in various natural language processing tasks. However, the question of how to further enhance LLMs' performance in specific task using prompting strategies remains a pivotal concern. This paper explores the enhancement of LLMs' performance in sentiment analysis through the application of prompting strategies. We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis: RolePlaying (RP) prompting and Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT prompting strategy which is a combination of RP prompting and CoT prompting. We conduct comparative experiments on three distinct domain datasets to evaluate the effectiveness of the proposed sentiment analysis strategies. The results demonstrate that the adoption of the proposed prompting strategies leads to a increasing enhancement in sentiment analysis accuracy. Further, the CoT prompting strategy exhibits a notable impact on implicit sentiment analysis, with the RP-CoT prompting strategy delivering the most superior performance among all strategies.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models
John F Kennedy High school Fremont, USA（约翰·F·肯尼迪高中弗里蒙特分校）；Queensland Academy for Science Mathematics and Technology（昆士兰科学数学与技术学院）；Research Spark Hub Inc（Research Spark Hub公司）
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms
The College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Harbin Institute of Technology（哈尔滨工业大学）
A Sentiment Consolidation Framework for Meta-Review Generation
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）
Causal Prompting for Implicit Sentiment Analysis with Large Language Models
School of Computing Technologies, RMIT University（RMIT大学计算机技术学院）；Institute of Innovation, Science and Sustainability, Federation University Australia（联邦大学澳大利亚创新、科学与可持续性研究所）；CSIRO’s Data61（CSIRO数据61）
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction
College of Software, Nankai University（南开大学软件学院）；College of Computer Science, Nankai University（南开大学计算机学院）；College of Artificial Intelligence, Tianjin University of Science and Technology（天津科技大学人工智能学院）
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
MAIS, Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Beijing Wenge Technology Co., Ltd（北京文格科技有限公司）
LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction
Media Informatics Group, University of Regensburg, Regensburg, Germany（莱茵河畔大学媒体信息学组）；Information Science Group, University of Regensburg, Regensburg, Germany（莱茵河畔大学信息科学组）
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；The Hong Kong Polytechnic University（香港理工大学）；College of Software Engineering, Zhengzhou University of Light Industry（郑州轻工业大学软件学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Information Technology, Halmstad University（哈尔姆斯塔德大学信息技术学院）
Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance
Indiana University Bloomington（印第安纳大学伯明顿分校）；Center for Complex Networks and Systems（复杂网络与系统中心）
The Ohio State University（俄亥俄州立大学）；Google Research（谷歌研究院）
Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resulting lack of high-quality labeled data stands in the way of conventional supervised learning methods. Instead, we approach this problem using semi-supervised learning with a large language model (LLM). Our pipeline generates weak financial sentiment labels for Reddit posts with an LLM and then uses that data to train a small model that can be served in production. We find that prompting the LLM to produce Chain-of-T…
Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）数据科学与分析学域）；Financial Technology Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）金融科技学域）
When Less Is Enough: Context Selection and Prompting Strategies for Bengali News Headline Generation
Charles Sturt University（查尔斯斯特大学）；Chittagong University of Engineering and Technology（吉大港工程技术大学）；Bangladesh Army University of Science and Technology（孟加拉国陆军理工大学）
