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.

A Regression Framework for Understanding Prompt Component Impact on LLM Performance
Mar 27, 2026Andrew Lauziere, Jonathan Daugherty, Taisa KushnerLLM Prompting LLM Interpretability
Galois, Inc.
When Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models
Mar 26, 2026Binesh Sadanandan, Vahid BehzadanPrompt Sensitivity LLM Prompting
SAIL Lab, University of New Haven, West Haven, CT 06516, USA
Can Small Models Reason About Legal Documents? A Comparative Study
Mar 26, 2026Snehit VaddiSmall Language Models Legal Reasoning
Independent Researcher
Navigating the Prompt Space: Improving LLM Classification of Social Science Texts Through Prompt Engineering
Mar 26, 2026Erkan Gunes, Christoffer Florczak, Tevfik Murat YildirimLarge Language Model-Based Text Classification LLM Prompting
Constructor University · Aalborg University · University of Stavanger
Does Structured Intent Representation Generalize? A Cross-Language, Cross-Model Empirical Study of 5W3H Prompting
Mar 26, 2026Peng GangStructured Prompting LLM Prompting
Huizhou Lateni AI Technology Co., Ltd., Huizhou, China · Huizhou University, Huizhou, China
Enhanced Mycelium of Thought (EMoT): A Bio-Inspired Hierarchical Reasoning Architecture with Strategic Dormancy and Mnemonic Encoding
Mar 25, 2026Florian Odi StummerLLM Prompting Memory-Augmented Language Models
Institute of General Medicine Martin Luther University Halle-Wittenberg, Germany
Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development
Mar 25, 2026Zongliang Ji, Ziyang Zhang, Xincheng Tan +5Clinical Decision Support Evidence-Grounded Generation
Google Research · University of Toronto, Canada · Vector Institute, Canada +1
Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
Mar 24, 2026Hyunwoo Kim, Munyoung Lee, Seung Hyub Jeon +1Semiconductor Manufacturing Predictive Process Monitoring
Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea
LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface
Mar 23, 2026Michael Hind, Basel Shbita, Bo Wu +5Structured Prompting LLM Prompting
IBM Research Yorktown Heights, New York · IBM Research San Jose, California · IBM Research Cambridge, Massachusetts
SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection
Mar 23, 2026Kexian Tang, Jiani Wang, Shaowen Wang +1Knowledge Augmentation for Language Models Synthetic Data Generation for Language Models
Institute for Interdisciplinary Information Sciences, Tsinghua University
Left Behind: Cross-Lingual Transfer as a Bridge for Low-Resource Languages in Large Language Models
Mar 22, 2026Abdul-Salem BeibitkhanLow-Resource Language Processing Multilingual Language Model Evaluation
North American University
Evaluating 5W3H Structured Prompting for Intent Alignment in Human-AI Interaction
Mar 19, 2026Peng GangStructured Prompting LLM Prompting
Huizhou Lateni AI Technology Co., Ltd., Huizhou, China · Huizhou University, Huizhou, China
Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models
Mar 18, 2026Mousa Salah, Amgad MuneerLLM Prompting LLM Reasoning
Department of Computer Engineering, Gujarat Technological University, India · The University of Texas MD Anderson Cancer Center, Texas, USA
Prompt Engineering for Scale Development in Generative Psychometrics
Mar 16, 2026Lara Lee Russell-Lasalandra, Hudson GolinoPrompt Engineering LLM Prompting
University of Virginia, Charlottesville, VA, USA
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Mar 16, 2026Yanick Zengaffinen, Andreas Opedal, Donya Rooein +3LLM Prompting Generative AI in Education
1ETH Zürich · 2Bocconi University · University of Central Florida
Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs
Mar 14, 2026Kai Wang, Haoyang You, Yang Zhang +1Large Language Model-Based Role-Play Simulation LLM Prompting
Harbin Institute of Technology, China · Macquarie University, Australia
Large Language Models and Scientific Discourse: Where's the Intelligence?
Mar 13, 2026Harry Collins, Simon ThorneScientific Reasoning in Language Models LLM Reliability
Institute of Education, UCL, 20 Bedford Way, London, WC1H OAL, UK. · School of Technologies, Cardiff Metropolitan University, 200 Western Avenue, Cardiff, CF5 2YB, UK.
Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction
Mar 12, 2026Khiem Le, Sreejata Dey, Marcos Martínez Galindo +4Molecular Property Prediction Knowledge Distillation
University of Notre Dame, IN, USA · IBM Research
Large Language Models for Biomedical Article Classification
Mar 12, 2026Jakub Proboszcz, Paweł CichoszFew-Shot Text Classification Large Language Model-Based Text Classification
Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Tasks
Mar 12, 2026Ofir MaromLLM Prompting LLM Decision-Making
Independent Researcher
PRISM- $Δ$ : Differential Subspace Steering for Prompt Highlighting in Large Language Models
Mar 11, 2026Yuyao Ge, Shenghua Liu, Yiwei Wang +5Language Model Steering LLM Prompting
♣Institute of Computing Technology, Chinese Academy of Sciences · △University of California, Merced
PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses
Mar 11, 2026Minki Hong, Eunsoo Lee, Sohyun Park +1LLM Prompting LLM Evaluation
Department of Computer Science and Artificial Intelligence, Dongguk University, South Korea
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
Mar 11, 2026Anita Yang, Krikamol Muandet, Michele Caprio +2Imprecise Probability LLM Uncertainty Estimation
Lattice Lab, Toyota Motor Corporation, Japan · Department of Computer Science, University of Tokyo, Japan · Rational Intelligence Lab, CISPA, Helmholtz Center for Information Security, Germany +3
Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts
Mar 10, 2026Hongbo Bo, Jingyu Hu, Weiru LiuMulti-Agent LLM Systems LLM Agents
University of Bristol, Bristol, United Kingdom
Facial-Expression-Aware Prompting for Empathetic LLM Tutoring
Mar 10, 2026Shuangquan Feng, Laura Fleig, Ruisen Tu +6Intelligent Tutoring Systems Emotion-Aware Dialogue Generation
Neurosciences Graduate Program, University of California San Diego, La Jolla, USA · Department of Cognitive Science, University of California San Diego, La Jolla, USA · Department of Computer Science and Engineering, University of California San Diego, La Jolla, USA +4
Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
Mar 10, 2026Nina Gerszberg, Janka Hamori, Andrew LoGender Bias in Language Models Algorithmic Bias
Massachusetts Institute of Technology Cambridge, Massachusetts, USA
Novel Semantic Prompting for Zero-Shot Action Recognition
Mar 9, 2026Salman Iqbal, Waheed RehmanZero-Shot Action Recognition Video Action Recognition
In-Context Reinforcement Learning for Tool Use in Large Language Models
Mar 9, 2026Yaoqi Ye, Yiran Zhao, Keyu Duan +4RL for Tool Use In-Context RL
National University of Singapore · Salesforce AI Research · University of California Berkeley +1
Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination Reduction
Mar 8, 2026Brian Freeman, Adam Kicklighter, Matt Erdman +1LLM Hallucination Mitigation LLM Prompting
Trane Technologies, Davidson NC, USA
Prompt Complexity Dilutes Structured Reasoning: A Follow-Up Study on the Car Wash Problem
Mar 7, 2026Heejin JoLLM Prompting LLM Reasoning
Independent Researcher

Mar 27, 2026J/K move · Enter open · S save

A Regression Framework for Understanding Prompt Component Impact on LLM Performance

Andrew Lauziere, Jonathan Daugherty, Taisa Kushner

Galois, Inc.

LLM Prompting LLM Interpretability Prompt Sensitivity

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As large language models (LLMs) continue to improve and see further integration into software systems, so does the need to understand the conditions in which they will perform. We contribute a statistical framework for understanding the impact of specific prompt features on LLM performance. The approach extends previous explainable artificial intelligence (XAI) methods specifically to inspect LLMs by fitting regression models relating portions of the prompt to LLM evaluation. We apply our method to compare how two open-source models, Mistral-7B and GPT-OSS-20B, leverage the prompt to perform a simple arithmetic problem. Regression models of individual prompt portions explain 72% and 77% of variation in model performances, respectively. We find misinformation in the form of incorrect example query-answer pairs impedes both models from solving the arithmetic query, though positive examples do not find significant variability in the impact of positive and negative instructions - these prompts have contradictory effects on model performance. The framework serves as a tool for decision makers in critical scenarios to gain granular insight into how the prompt influences an LLM to solve a task.