Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying them into components such as claims and premises. While research on this subtask remains relatively limited compared to other AM tasks, most existing approaches formulate it as a simplified sequence labeling problem, component classification, or a pipeline of component segmentation followed by classification. In this paper, we propose a novel approach based on instruction-tuned Large Language Models (LLMs) using compact instruction-based prompts, and reframe ACD as a language generation task, enabling arguments to be identified directly from plain text without relying on pre-segmented components. Experiments on standard benchmarks show that our approach achieves higher performance compared to state-of-the-art systems. To the best of our knowledge, this is one of the first attempts to fully model ACD as a generative task, highlighting the potential of instruction tuning for complex AM problems.
WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays
Department of Informatics, King’s College London（伦敦国王学院信息学系）；Waseda University（早稻田大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；University of Sheffield（谢菲尔德大学）；Hosei University（法政大学）
From Instruction to Output: The Role of Prompting in Modern NLG
Faculty of Information Technology, Monash University（莫纳什大学信息科技学院）；School of Computer, Data and Mathematical Sciences, Western Sydney University（西澳大学计算机、数据与数学科学学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）
Large Language Models are affected by the phenomena of memorizing and forgetting their training data. But how do these vary by model size? We work towards this question by investigating how the model size affects the model's ability to discriminate a word's meaning in a given context. We introduce a dataset called DeltaWords, which evaluates a model's ability to follow instructions to select a sentence which replaces the target word with its antonym. We show a weak inverse scaling trend, where task accuracy degrades as model size increase, under extremely few-shot prompting regimes. We show th…
Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions. In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor. We collect 64,000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth. This set is then expanded by prompting the model to rephrase each instruction, creating …
Controllable Navigation Instruction Generation with Chain of Thought Prompting
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
BNU-HKBU United International College（北师港浸大）；Hong Kong Baptist University（香港浸会大学）；Beijing Normal University at Zhuhai（北京师范大学珠海校区）；The Chinese University of Hong Kong（香港中文大学）
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；Huawei Technologies Ltd.（华为技术有限公司）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
Carnegie Mellon University（卡内基梅隆大学）；University of Michigan（密歇根大学）；University of Toronto（多伦多大学）；Colgate University（科尔盖特大学）；University of Toronto Mississauga（多伦多大学密西根分校）
LLMs can be easily Confused by Instructional Distractions
IPAI, Seoul National University（IPAI，首尔国立大学）；LG AI Research（LG人工智能研究所）；Dept. of ECE, Seoul National University（电子工程系，首尔国立大学）；SNU-LG AI Research Center（SNU-LG人工智能研究所）
PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
Department of Computer Science and Information Engineering, National Taiwan University（国立台湾大学计算机科学与资讯工程系）；National Taiwan University AI Center of Research Excellence（国立台湾大学人工智能研究中心）
