LLM Prompting

LLM: Large Language Model

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22 papers in the last four weeks, up 22% on the four weeks before. 0.2% of all new papers.

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Mind Your Tone: Does Tone Alter LLM Performance?
May 27, 2026Om Dobariya, Akhil KumarLLM Evaluation LLM Prompting
Pennsylvania State University University Park, PA, USA
Reasoning that Travels: Dissecting How Chain-of-Thought Transfers Across Models
May 27, 2026Xinyuan Cheng, Beiduo Chen, Philipp Mondorf +1LLM Prompting LLM Reasoning
UMaiNLP, Center for Information and Language Processing, LMU Munich, Germany · ÆMunich Center for Machine Learning, Germany
Prompt Codebooks: Discrete Compositional Optimization for Language Model Instruction Refinement
May 27, 2026Jyotirmoy Nath, Neeraj Kumar, Brejesh LallLLM Prompting Large Language Model-Guided Optimization
1IIT Delhi, India
Whose Name Comes Up? III: Persona Prompting Effects in LLM-Based Scholar Recommendation
May 27, 2026Annabella Sánchez-Guzmán, Lukas Eberhard, Denis Helic +1LLM Auditing LLM Prompting
Escuela Superior Polit´ecnica del Litoral · Graz University of Technology · Complexity Science Hub
Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis
May 27, 2026Nils Constantin Hellwig, Niklas Donhauser, Jakob Fehle +2LLM Prompting Few-Shot Prompting
1Media Informatics Group, University of Regensburg, Germany · 2Information Science Group, University of Regensburg, Germany
Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis
May 27, 2026Moe Nagao, Koichiro Terao, Mikio Nakano +1Large Language Model-Based Role-Play Simulation LLM Prompting
Okayama Prefectural University · AI & Humans Lab · C4A Research Institute
Learning to Translate from Soft to Hard LLM Prompts
May 26, 2026Pitipat Kongsomjit, Suryansh Goyal, Jacob WhitehillLLM Prompting LLM Interpretability
Worcester Polytechnic Institute
LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring
May 26, 2026Unggi Lee, Minchul Shin, Yeil Jeong +5Intelligent Tutoring Systems LLM Prompting
Korea University Sejong Campus · 1Korea University Sejong Campus · Gyeonggi Institute of Education +9
Strategies for Guiding LLMs to Use Software Design Patterns: A Case of Singleton
May 26, 2026Viktor Kjellberg, Farnaz Fotrousi, Miroslaw StaronLLM Prompting Code Generation
University of Gothenburg and Chalmers University of Technology Gothenburg, Sweden
Simulating Human Memory with Language Models
May 25, 2026Qihan Wang, Nicholas Tomlin, Michael Hu +2LLM Evaluation Memory-Augmented Language Models
NYU · UMass Amherst
Re-defining Humor Data Objects for AI Humor Research
May 24, 2026Anna Arnett, Bang Nguyen, Meng JiangLLM Prompting Synthetic Data Generation for Language Models
Department of Computer Science and Engineering, University of Notre Dame
Code2UML: Agentic LLMs with context engineering for scalable software visualization
May 23, 2026Alin-Gabriel Văduva, Anca-Ioana Andreescu, Simona-Vasilica Oprea +1Software Engineering Agents LLM Prompting
Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, no. 6 Piaţa Romană, Bucharest, Romania
An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
May 22, 2026Mohammed Kharma, Ahmed Sabbah, Mohammad Alkhanafseh +2LLM Prompting LLM Security
Department of Computer Science, Birzeit University, Birzeit, Palestine · King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia · University of Central Florida, Orlando, FL, USA
As X, Do Y: How Persona and Task Combine in Instruction-Tuned LLMs
May 22, 2026Eric XuLLM Prompting LLM Interpretability
Independent Researcher
Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs
May 21, 2026Manuel Noah Riesen, Peter Alfred von NiederhäusernLLM Reasoning with Graphs LLM Prompting
School of Engineering and Computer Science Bern University of Applied Sciences Bern, Switzerland
Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity
May 21, 2026Geoffrey Martin, Xuan Zhong Feng, Yifan PengLLM Prompting LLM Information Extraction
Department of Population Health Sciences, Weill Cornell Medicine, New York, USA · Systems Engineering, Cornell University, New York, USA
Reflective Prompt Tuning through Language Model Function-Calling
May 20, 2026Farima Fatahi Bayat, Moin Aminnaseri, Pouya Pezeshkpour +1LLM Self-Refinement LLM Prompting
Megagon Labs
PromptNCE: Conditional Probabilities and PMI Using Only LLMs and Contrastive Estimation Prompts
May 20, 2026Juliette Woodrow, Chris PiechLLM Prompting Conditional Distribution Estimation
Department of Computer Science Stanford University
Strategy-Induct: Task-Level Strategy Induction for Instruction Generation
May 20, 2026Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi ChenLLM Prompting Few-Shot 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
Less Back-and-Forth: A Comparative Study of Structured Prompting
May 19, 2026Saurav Ghosh, Gabriella Polach, Abdou SowLLM Evaluation Structured Prompting
Washington University in St. Louis Saint Louis, Missouri, USA
MixRea: Benchmarking Explicit-Implicit Reasoning in Large Language Models
May 19, 2026Yuanqing Cai, Ziyi Huang, Minhao Liu +3LLM Evaluation LLM Prompting
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China
Occupational Prompting Reveals Cultural Bias in Large Language Models
May 19, 2026Maksim E. Eren, Andrea Brennen, Ryan C. Barron +1LLM Prompting Social Bias in Language Models
Computational Intelligence & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · IQT, USA. · Advanced Research in Cyber Systems, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
Structured Layout Priors for Robust Out-of-Distribution Visual Document Understanding
May 19, 2026Peter El Hachem, Ahmed Nassar, A. Said Gurbuz +2VLM Robustness LLM Prompting
ETH Zurich Rämistrasse, Switzerland · IBM Research Rüschlikon, Switzerland
Prompting language influences diagnostic reasoning and accuracy of large language models
May 18, 2026Adrien Bazoge, Josselin Corvellec, Sofiane Djillali Sid-Ahmed +1Multilingual Language Model Evaluation LLM Prompting
Data Clinic, Nantes University Hospital, France
The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration
May 18, 2026Cansu Koyuturk, Sabrina Guidotti, Dimitri OgnibeneLLM Sycophancy LLM Prompting
Università degli Studi di Milano-Bicocca, Italy
Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
May 18, 2026Amritansh Maurya, Navjot Singh, Mohammed Javed +1Table QA Structured Prompting
Vision Intelligence Lab, IIIT Allahabad, Prayagraj, India · iMeDIA Lab, IIIT Allahabad, Prayagraj, India · CV:HCI Lab, Karlsruhe Institute of Technology, Karlsruhe, Germany
ACIL: Auto Chain of Thoughts for In-Context Learning
May 16, 2026Rui ChuLLM Prompting CoT Reasoning
PromptDecipher: Supporting AI Tutor Authoring Through Editable Simulated Interactions
May 15, 2026Miina Koyama, Ruiwei Xiao, John StamperIntelligent Tutoring Systems LLM Prompting
Carnegie Mellon University Pittsburgh, Pennsylvania, USA
LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations
May 15, 2026Henry Salgado, Meagan R. Kendall, Martine Ceberio +1LLM Prompting LLM Context Management
Predicting Performance of Symbolic and Prompt Programs with Examples
May 15, 2026Chengqi Zheng, Keya Hu, Shuzhi Liu +3LLM Prompting
Nanayang Technological University, Singapore · Massachusetts Institute of Technology, USA · Cornell University, USA

May 27, 2026, cs.AIJ/K move · Enter open · S save

Mind Your Tone: Does Tone Alter LLM Performance?

Om Dobariya, Akhil Kumar

Pennsylvania State University University Park, PA, USA

LLM Evaluation LLM Prompting Language Model Robustness

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The use of Large Language Models (LLMs) is proliferating, yet their performance is observed to vary based on prompting styles and tones. In this study, we investigate both whether and how tonal variations in prompts lead to disparate LLM accuracy for objective multiple-choice questions. We use two datasets: a 50-base question dataset with five tone variants and a 570-base question MMLU subset spanning 57 subjects with seven tone variants. Experiments were conducted to evaluate the performance of four cost-efficient, popular LLMs: ChatGPT-4o, ChatGPT-5-nano, Gemini 2.5 Flash, and Gemini 2.5 Flash Lite. Across models, tonal effects are systematic but highly model-dependent. Some models show small, yet statistically significant, shifts, while others exhibit large accuracy swings across tones. Further, we identify subject-level differences in tone sensitivity and present a routing framework to explain how tones may attune internal reasoning modes. Our findings caution users against assuming tone-robust reliability in LLM deployments.

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