LLM Prompting

LLM: Large Language Model

The field studies how the wording and structure of inputs affect what language models understand, generate, and reliably follow. Common approaches include zero- and few-shot in-context learning, chain-of-thought and task decomposition, retrieval-augmented context, and structured prompts using schemas, roles, or constraints. Researchers also develop automated prompt search, feedback-driven refinement, and methods that adapt examples or context to each input, testing them on tasks such as reasoning, classification, code generation, and information extraction. Prompting can improve performance without changing model weights, but its effects vary across models and tasks: added instructions can hurt, and gains in accuracy may trade off with reliability, diversity, safety, or cost.

Latest papers

431 papers34 papers in the last four weeks, down 13% on the four weeks before. 0.2% of all new papers.

Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact
Apr 25, 2026Kiyoshige Garces, Gloria Milena Fernandez-Nieto, Linxuan Zhao +4LLM Inference Efficiency LLM Prompting
RMIT University · Monash University · Adelaide University +2
From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verification
Apr 24, 2026Md Erfan, Md Kamal Hossain Chowdhury, Ahmed Ryan +1LLM Prompting Code Generation
Department of Computer Science, The University of Alabama, Tuscaloosa, USA · Alabama Water Institute, The University of Alabama, Tuscaloosa, USA
Learning Evidence Highlighting for Frozen LLMs
Apr 24, 2026Shaoang Li, Yanhang Shi, Yufei Li +10Long-Context QA LLM Prompting
Stony Brook University · Meta AI
Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations
Apr 24, 2026Anna Arnaudo, Riccardo Coppola, Maurizio Morisio +4Requirements Engineering LLM Prompting
Department of Control and Computer Engineering, Politecnico di Torino · Politecnico di Torino
Voice Under Revision: Large Language Models and the Normalization of Personal Narrative
Apr 24, 2026Tom van NuenenLLM Prompting
Social Sciences D-Lab, University of California, Berkeley
Shared Lexical Task Representations Explain Behavioral Variability In LLMs
Apr 23, 2026Zhuonan Yang, Jacob Xiaochen Li, Francisco Piedrahita Velez +5Prompt Sensitivity LLM Prompting
Brown University · MIT · Northeastern
When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation
Apr 23, 2026Anamta Khan, Ratna Kandala, Deepti +2LLM Prompting Automated Fact-Checking
University of Michigan · University of Kansas · IIT Jodhpur
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Apr 23, 2026Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin +6LLM Prompting Automated Feature Engineering
University of Oxford · Vela Research · Amazon
Unlocking the Power of Large Language Models for Multi-table Entity Matching
Apr 23, 2026Yingkai Tang, Taoyu Su, Wenyuan Zhang +2Entity Resolution LLM Prompting
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · School of Information Engineering China University of Geosciences Beijing, Beijing, China
Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
Apr 22, 2026Xuhong He, To Eun Kim, Maik Fröbe +3LLM Prompting Multilingual IR
Carnegie Mellon University · Pittsburgh, PA, USA · Friedrich-Schiller-Universität Jena +5
BODHI: Precise OS Kernel Specification Inference
Apr 22, 2026Zhiming Chang, Ziyang LiKnowledge Augmentation for Language Models LLM Prompting
Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore, US · Department of Computer Science Johns Hopkins University Baltimore, US
Can "AI" Be a Doctor? A Study of Empathy, Readability, and Alignment in Clinical LLMs
Apr 22, 2026Mariano Barone, Francesco Di Serio, Roberto Moio +4Healthcare LLM Prompting
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, Naples, 80125, Italy. · Department of Translational Medical Sciences, University of Campania ”Luigi Vanvitelli”, Via Leonardo Bianchi, Naples, 80131, Italy. · Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, 2309 Sheridan Rd, Evanston, 60201, IL, United States.
Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs
Apr 22, 2026Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani +2LLM Evaluation Educational Assessment
Leibniz Information Centre for Science and Technology (TIB) · University of Genoa
Benchmarking Large Language Models for Safety Data Extraction
Apr 22, 2026Jonas Grill, Thomas Bayer, Sören BerlingerLLM Evaluation LLM Prompting
1SAP SE, Germany. · Institute for Digital Transformation, Ravensburg-Weingarten University.
The signal is the ceiling: Measurement limits of LLM-predicted experience ratings from open-ended survey text
Apr 21, 2026Andrew Hong, Jason Potteiger, Luis E. ZapataLLM Evaluation LLM Prompting
Dimension Labs
Less Is More: Cognitive Load and the Single-Prompt Ceiling in LLM Mathematical Reasoning
Apr 20, 2026Manuel Israel CazaresPrompt Sensitivity LLM Prompting
Bytepro AI Mazatlán, Sinaloa, Mexico
Understanding the Prompt Sensitivity
Apr 20, 2026Yang Liu, Chenhui ChuPrompt Sensitivity LLM Prompting
Kyoto University
Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design
Apr 20, 2026Xinyao Zhang, Nicole Sonne Heckmann, Manuela Del Castillo Suero +2LLM Evaluation Healthcare
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
Semantic Density Effect (SDE): Maximizing Information Per Token Improves LLM Accuracy
Apr 19, 2026Amr AhmedLLM Prompting Prompt Optimization
Public Health Department, Riyadh First Health Cluster · Ministry of Health, Saudi Arabia
Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
Apr 19, 2026Elaf Alhazmi, Quan Z. Sheng, Wei Emma ZhangLLM Prompting In-Context Learning
School of Computing, Macquarie University, Australia · College of Engineering and Computing in Al-Lith, Umm Al-Qura University, Saudi Arabia · School of Computer and Mathematical Sciences, Adelaide University, Australia
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 Cognitive Bias in Language Models
Collegium Helveticum at ETH Zurich, Switzerland · University of Zurich, Switzerland · University of Italian-speaking Switzerland, Switzerland
Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
Apr 17, 2026Nicolò Pagan, Christopher Barrie, Chris Andrew Bail +1LLM Prompting Social Bias in Language Models
University of Zurich, Department of Informatics, Zurich, CH · New York University, Department of Sociology, New York, USA · University of Oxford, Department of Sociology, Oxford, UK +2
Schema-Key Wording as an Instruction Channel in Structured Generation under Constrained Decoding
Apr 16, 2026Yifan LeConstrained Generative Modeling Constrained Decoding
Independent Researcher
StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation
Apr 16, 2026Geonhui Jang, Dongyoon Han, YoungJoon YooLLM Prompting LLM Code Generation
Dept. of Artificial Intelligence, Chung-Ang University · NAVER AI Lab
Prompt-Driven Code Summarization: A Systematic Literature Review
Apr 16, 2026Afia Farjana, Zaiyu Cheng, Antonio MastropaoloLLM Evaluation LLM Prompting
William & Mary, USA
RPS: Information Elicitation with Reinforcement Prompt Selection
Apr 15, 2026Tao Wang, Jingyao Lu, Xibo Wang +5LLM Prompting Reinforcement Learning
Department of Computer Science, Southwestern University of Finance and Economics, China · Eindhoven University of Technology, Netherlands · DreamSoul, China +4
Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs
Apr 14, 2026Alkid Baci, Luke Friedrichs, Caglar Demir +2KG Completion LLM Prompting
Department of Computer Science Paderborn University Warburger Str. 100, 33098 Paderborn, Germany
Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe
Apr 14, 2026Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit MitraLow-Resource Language Processing LLM Prompting
Carnegie Mellon University Africa, Kigali, Rwanda · Centre for Text Technology, North-West University, Potchefstroom, South Africa
Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models
Apr 14, 2026Xudong Wang, Chaoning Zhang, Chenghao Li +10Automatic Prompt Optimization LLM Prompting
Kyung Hee University · University of Electronic Science and Technology of China · Nota Inc. +1

Apr 25, 2026J/K move · Enter open · S save

Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact

Kiyoshige Garces, Gloria Milena Fernandez-Nieto, Linxuan Zhao, Sachini Samaraweera+3

RMIT University · Monash University · Adelaide University+2

LLM Inference Efficiency LLM Prompting LLM-Assisted Annotation

Open paper AskPDF arXiv

Research shows that dialogue, the interactive process through which participants articulate their thinking, plays a central role in constructing shared understanding, coordinating action, and shaping learning outcomes in teams. Analysing dialogue content has been central to advancing team learning theory and informing the design of computer-supported collaborative learning environments, yet this progress has depended on labour-intensive qualitative coding. LLMs offer new possibilities for automating and enhancing the dialogue layer within emerging multimodal learning analytics approaches, with recent studies showing that they can approximate human coding through few-shot prompting. However, prior work has focused on replicating human coding accuracy for research purposes, rather than addressing a more educationally consequential question: how can we design prompts that allow an LLM to label team dialogue accurately and fast enough to be useful in real settings, such as in-person healthcare simulations, where results must be returned quickly and computational cost and sustainability also matter? This paper investigates how prompt design and batching strategies can be optimised to balance coding accuracy, processing time, and environmental impact in team-based healthcare simulation debriefing. Using a dataset of 11,647 utterances coded across 6 dialogue constructs, we compared 4 prompt designs across varying batch sizes, evaluating coding performance, processing time, and energy consumption, as well as the trade-offs between these metrics. Results indicate that increasing batch size improves speed and reduces energy use, but negatively impacts coding performance. Beyond demonstrating the feasibility of LLM-based qualitative analysis, this study offers practical guidance for scaling dialogue analytics in contexts where timeliness, privacy, and sustainability are critical.