Prompt Engineering

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10 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models
Apr 25, 2026Gautam Kishore Shahi, Oliver HummelLLM Evaluation LLM Prompting
Technische Hochschule Mannheim, Germany
From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
Apr 23, 2026Katharina Prasse, Steffen Jung, Isaac Bravo +4VLM Evaluation Social Media Analysis
Chair of Machine Learning, University of Mannheim Mannheim, BW 68131, Germany · Department of Governance, Technical University Munich München, BA 80333, Germany · Clausthal University of Technology Clausthal-Zellerfeld, NI 38678, Germany +1
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
Apr 22, 2026Gricel Vázquez, Alexandros Evangelidis, Sepeedeh Shahbeigi +2Explainable Artificial Intelligence Human-in-the-Loop AI
University of York, UK · Cyprus University of Technology, Cyprus
Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication
Apr 21, 2026Mohamed Afane, Emily Robitschek, Derek Ouyang +1AI-Assisted Decision Making Structured Prompting
Stanford University · Stanford, CA, USA
Long-Text-to-Image Generation via Compositional Prompt Decomposition
Apr 20, 2026Jen-Yuan Huang, Tong Lin, Yilun DuT2I Generation Compositional T2I Generation
Peking University · State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology. · Harvard University
Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
Apr 19, 2026Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez +1LLM Inference Efficiency Self-Consistency Decoding
Carnegie Mellon University Qatar · Qatar Computing Research Institute
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
Apr 18, 2026Wei-Chi Wu, Sheng-Lun Wei, Hen-Hsen Huang +1Multilingual Language Model Evaluation LLM Prompting
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan · Institute of Information Science, Academia Sinica, Taiwan · AI Research Center (AINTU), National Taiwan University, Taiwan
Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering
Apr 18, 2026Francesco Sovrano, Gabriele Dominici, Alberto BacchelliSoftware Engineering LLM Prompting
Collegium Helveticum at ETH Zurich, Switzerland · University of Zurich, Switzerland · University of Italian-speaking Switzerland, Switzerland
Prompt-Driven Code Summarization: A Systematic Literature Review
Apr 16, 2026Afia Farjana, Zaiyu Cheng, Antonio MastropaoloLLM Evaluation LLM Prompting
William & Mary, USA
Bridging Individual and Collective Realism in LLM-Based Human Mobility Simulation via Mobility Scaling-Law Guidance
Feb 17, 2026Hua Yan, Heng Tan, Yu YangHuman Behavior Simulation Large Language Model-Based Social Simulation
Lehigh University · Bethlehem, USA
Do Language Models Reason Across Languages?
Jan 10, 2026Yan Meng, Wafaa Mohammed, Christof MonzCoT Faithfulness Cross-Lingual Reasoning in Language Models
Language Technology Lab · University of Amsterdam
AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models
Nov 3, 2025Sarthak Mishra, Rishabh Dev Yadav, Avirup Das +3Aerial Robotics Structured Prompting
Robotics Research Center, IIIT Hyderabad, India · Department of Computer Science, University of Manchester, UK · Newcastle University, UK
LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
Aug 22, 2025Zirui Li, Stephan Husung, Haoze WangModel-Based Systems Engineering Prompt Engineering
Product and Systems Engineering Group Technische Universität Ilmenau Ilmenau, Germany
Interaction Techniques that Encourage Longer Prompts Can Improve Psychological Ownership when Writing with AI
Jul 4, 2025Nikhita Joshi, Daniel VogelHuman-AI Interaction Human-AI Co-Creation
Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada · LISN, Université Paris-Saclay, CNRS, Inria
Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
May 22, 2025Peyman Baghershahi, Sourav MedyaSource-Free Domain Adaptation Unsupervised Domain Adaptation
Department of Computer Science University of Illinois Chicago
Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation
Date pendingMd Tariquzzaman, Md Farhan Ishmam, Saiyma Sittul Muna +2VLM Evaluation Vision-Language Models
Department of Computer Science and Engineering Islamic University of Technology, Bangladesh

Apr 25, 2026, cs.IRJ/K move · Enter open · S save

Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models

Gautam Kishore Shahi, Oliver Hummel

Technische Hochschule Mannheim, Germany

LLM Evaluation LLM Prompting Text Classification Prompt Engineering

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The relentless expansion of scientific literature presents significant challenges for navigation and knowledge discovery. Within Research Information Retrieval, established tasks such as text summarization and classification remain crucial for enabling researchers and practitioners to effectively navigate this vast landscape, so that efforts have increasingly been focused on developing advanced research information systems. These systems aim not only to provide standard keyword-based search functionalities but also to incorporate capabilities for automatic content categorization within knowledge-intensive organizations across academia and industry. This study systematically evaluates the performance of off-the-shelf Large Language Models (LLMs) in analyzing scientific texts according to a given classification scheme. We utilized the hierarchical ORKG taxonomy as a classification framework, employing the FORC dataset as ground truth. We investigated the effectiveness of advanced prompt engineering strategies, namely In-Context Learning (ICL) and Prompt Chaining, and experimentally explored the influence of the LLMs' temperature hyperparameter on classification accuracy. Our experiments demonstrate that Prompt Chaining yields superior classification accuracy compared to pure ICL, particularly when applied to the nested structure of the ORKG taxonomy. LLMs with prompt chaining outperform the state-of-the-art models for domain (1st level) prediction and show even better performance for subject (2nd level) prediction compared to the older BERT model. However, LLMs are not yet able to perform well in classifying the topic (3rd level) of research areas based on this specific hierarchical taxonomy, as they only reach about 50% accuracy even with prompt chaining.

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