Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
The Chinese University of Hong Kong（香港中文大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；National University of Singapore（新加坡国立大学）；Peng Cheng Laboratory（鹏城实验室）
Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, Kam-Fai Wong
Large Language Models (LLMs), such as \texttt{ChatGPT}, greatly empower dialogue systems with strong language understanding and generation capabilities. However, most of the previous works prompt the LLMs to directly generate a response based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context. Such in-depth dialogue scenarios are challenging for existing LLMs to figure out the user's hidden needs and respond satisfactorily through a single-step inference. To this end, we propose a novel linguistic cue-based chain-of-thoughts (\textit{Cue}-CoT), which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging response. To evaluate the approach, we build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English, targeting 3 major linguistic cues during the conversation: \textit{personality}, \textit{emotion}, and \textit{psychology}. We conduct extensive experiments on the proposed benchmark with 5 LLMs under both zero-shot and one-shot settings. Empirical results demonstrate our proposed \textit{Cue}-CoT method outperforms standard prompting methods in terms of both \textit{helpfulness} and \textit{acceptability} on all datasets.
Shanghai Jiao Tong University（上海交通大学）；Amazon Web Services（亚马逊云科技）
Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting. CoT prompting has two major paradigms. One leverages a simple prompt like "Let's think step by step" to facilitate step-by-step thinking before answering a question. The other uses a few manual demonstrations one by one, each composed of a question and a reasoning chain that leads to an answer. The superior performance of the second paradigm hinges on the hand-crafting of task-specific demonstration…
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）
Chain-of-thought (CoT) prompting enables large language models (LLMs) to solve complex reasoning tasks by generating an explanation before the final prediction. Despite it's promising ability, a critical downside of CoT prompting is that the performance is greatly affected by the factuality of the generated explanation. To improve the correctness of the explanations, fine-tuning language models with explanation data is needed. However, there exists only a few datasets that can be used for such approaches, and no data collection tool for building them. Thus, we introduce CoTEVer, a tool-kit for…
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Key Laboratory of Machine Perceptron (MOE), Peking University（北京大学机器感知教育部重点实验室）
Revisiting Direct Speech-to-Text Translation with Speech LLMs: Better Scaling than CoT Prompting?
Barcelona Supercomputing Center, Spain（巴塞罗那超级计算中心，西班牙）；Universitat Politècnica de Catalunya, Spain（加泰罗尼亚理工大学，西班牙）；DFKI GmbH, Saarland Informatics Campus, Saarbrücken, Germany（德意志联邦计算机研究中心（DFKI） GmbH，萨尔布吕肯，德国）
ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Information, Renmin University of China（中国人民大学信息学院）；DIRO, Université de Montréal（蒙特利尔大学计算机科学与运筹学系）
Large language models that are capable of zero or few-shot prompting approaches have given rise to the new research area of prompt engineering. Recent advances showed that for example Chain-of-Thought (CoT) prompts can improve arithmetic or common sense tasks significantly. We explore how such approaches fare with legal reasoning tasks and take the COLIEE entailment task based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning approaches. Our findings show that while CoT prompting and fine-tuning with explanations approaches show improvements, the best results are produced…
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）
BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progress on this benchmark, with the best model in the BIG-Bench paper outperforming average reported human-rater results on 65% of the BIG-Bench tasks via few-shot prompting. But on what tasks do language models fall short of average human-rater performance, and are those tasks actually unsolvable by current language models? In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we c…
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Alibaba Cloud Computing（阿里巴巴云计算）；Shanghai Jiao Tong University（上海交通大学）；College of Software, Zhejiang University（浙江大学软件学院）
Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research（武汉人工智能研究院）
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；National University of Singapore（新加坡国立大学）；University of Electronic Science and Technology of China（电子科技大学）；Beijing University of Posts and Telecommunieations（北京邮电大学）；Singapore Management University（新加坡管理大学）
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Singapore Management University（新加坡管理大学）；Southwest Jiaotong University（西南交通大学）；Singapore University of Technology and Design（新加坡科技设计大学）；East China Normal University（华东师范大学）
