LLM Prompting

LLM: Large Language Model

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Overview

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

CardsList

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

  1. Generative artificial intelligence prompting strategies among Chinese university students across academic tasks

    Oct 29, 2026Lei Fan, Fangxue LiuLLM PromptingGenerative AI in Education

    Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China · Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

  2. PromptSense: Sentiment-Aware Mental Health Chatbot with Dynamic Prompting

    Oct 27, 2026Sugi Lee, Jianlong Zhou, Jing sUNMental Health Conversational AgentsEmotion-Aware Dialogue Generation

    Transdisciplinary School, University of Technology Sydney, Sydney, Australia · Data Science Institute, University of Technology Sydney, Sydney, Australia

  3. Position Matters More Than Channel: Talking a Language Model Out of a Correct Answer

    Oct 26, 2026Xinyi NiLLM PromptingKnowledge Conflicts in Language Models

    Brandeis University

  4. Large Language Models: A Plain-Language Overview for Engineers

    Oct 25, 2026Bahaeddin TürkoğluLLM TrainingLLM Prompting

    Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara, Türkiye

  5. Scaling Qualitative Analysis with Large Language Models: Development and Validation of the Smart Coder Framework for Human-LLM Collaborative Coding

    Oct 23, 2026Cheng Tang, Kun Wang, Yaxuan Yang +4LLM-Assisted AnnotationLLM Prompting

    Department of Educational Psychology, University of Georgia, Athens, GA, USA · Rethink Learning Labs, Woodcliff Lake, NJ, USA · Department of Early Childhood/Elementary Education, Penn State Abington, Abington, PA, USA

  6. The Language of Instruction: Genre and Generative AI

    Oct 23, 2026James E. DobsonLLM PromptingInstruction Tuning

    Department of English and Creative Writing Dartmouth College

  7. How Prompt Style Affects AI Accuracy and Speed in Competitive Programming

    Oct 21, 2026Saad B. Alarifi, Mohammed A. AlamLLM PromptingPrompt Sensitivity

    Riyadh Schools Riyadh, Saudi Arabia · Department of Computer Science Northwestern University Evanston, IL, USA

  8. Where Do LLMs Learn to Recommend AI?

    Oct 21, 2026Benaya Trabelsi, Jonathan Shaki, Sarit KrausLarge Language Model-Based RecommendationLLM Prompting

    Bar-Ilan University

  9. A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought Prompting

    Oct 21, 2026Ibrahim Khan, Thai Van Nguyen, Yi Xia +5LLM PromptingAdaptive Reasoning in Language Models

    College of Information Science and Technology, Ritsumeikan University, Japan · Graduate School of Information Science and Technology, Ritsumeikan University, Japan · Independent Researcher, and an alumnus of the Graduate School of Information Science and Technology, Ritsumeikan University, Japan +1

  10. The Instruction Tax on Factual Recall: Base Models Can Beat Their Instruction-Tuned Counterparts

    Oct 20, 2026Christophe Khalil, Aya MouradFactual Knowledge in Language ModelsInstruction Tuning

    Sorbonne Université, Paris, France

  11. A Cross-Domain Evaluation of Prompting Techniques for Large Language Models

    Oct 17, 2026Kevin Krazius, Tim Schlippe, Paul LibbrechtLLM PromptingLLM Evaluation

    IU International University of Applied Sciences, Germany

  12. Best of Both Worlds: Fine-Tuned Small Models Complement LLMs for Cost and Performance Benefits

    Oct 16, 2026Shivam Mathur, Sri Harish Popuri, Priya Krishnan Sundararajan +2Text ClassificationLLM Inference Efficiency

    Walmart Inc.

  13. LocalIntent: Outcome-Specific Structured Generation for Backend-Compatible Smart Building Automation with Local LLMs

    Oct 16, 2026Tianyang Liu, Jiayue Wang, Zhanhao Cheng +4LLM PromptingLanguage Model-Based Control

    Beijing Dublin International College, University College Dublin, Beijing, China · School of Computer Science, University College Dublin, Dublin, Ireland

  14. Reasoning Models Can Control Their Chains of Thought When They Tell Themselves To

    Oct 16, 2026Uriel Dolev, Idan Schwartz, Yoav GoldbergLanguage Model Reasoning EvaluationLLM Prompting

    Bar-Ilan University · Allen Institute for AI

  15. PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems

    Oct 15, 2026Shuo Lin, Jinzhe Li, Chenlu Guo +3Text-to-SQLText-to-SQL Evaluation

    School of Artificial Intelligence, Jilin University · International Center of Future Science, Jilin University · Bilibili Inc. +2

  16. Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction

    Oct 15, 2026SeungHyun Yi, Seungjun Yi, Sriram Sattiraju +12LLM PromptingEmotion Recognition

    Department of Computer Science, Kyung Hee University, Yongin, South Korea · Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA · Department of Computer Science, University of Texas at Austin, Austin, TX, USA +9

  17. PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning

    Oct 15, 2026Max Rosenblattl, Nikhil Krishnaswamy, Leon Nissen +9Time Series Language ModelingOn-Device Language Model Inference

    Stanford University, USA · CHI, Technical University of Munich University Hospital, Germany · ETH Zurich, Switzerland +3

  18. RLPE: Teaching LLMs to Prompt with Reinforcement Learning

    Oct 13, 2026Rahul Singhal, Karime Maamari, Pradyumna TambwekarLLM PromptingAutomatic Prompt Optimization

  19. Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs

    Oct 13, 2026Mikhail Aleksandrov, Nail Bashirov, Maksim Ivanov +6Prefix TuningLLM Prompting

    BRAIn Lab

  20. Encoding Context into LLM Weights: What Makes It Context?

    Oct 13, 2026Yue Wang, Qizhou Wang, Feng Liu +3LLM Context ManagementTest-Time Training

    The University of Melbourne · RIKEN Center for Advanced Intelligence Project · The University of Tokyo +1

  21. Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows

    Oct 12, 2026Bilel Abderrahmane Benziane, Xavier ScheplerText-to-SQLSmall Language Models

    Data Lab, Generix Group, Paris, France

  22. Lexical Redefinition Reveals Context-Sensitive Instruction Following

    Oct 12, 2026Roksana Goworek, Haim DubossarskyLLM PromptingPragmatic Reasoning in Language Models

    Queen Mary University of London · University of Cambridge

  23. Dependency Reasoning for Aspect Sentiment Quadruple Prediction

    Oct 10, 2026Shima Esfandyari, Seywan Soleymanbaigi, Fardin Akhlaghian Tab +2Aspect-Based Sentiment AnalysisLLM Prompting

    Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran · University of Oulu, Finland

  24. Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction

    Oct 9, 2026Jiwon Jeon, Myungsik Cho, Jeonghye Kim +1LLM Self-CorrectionLLM Reasoning

    Korea Advanced Institute of Science and Technology (KAIST)

  25. From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification

    Oct 7, 2026Yue Qiu, Zekang Du, Yiqun Diao +2Tabular ClassificationDecision Tree Learning

    School of Computer Science and Technology, Huazhong University of Science and Technology · School of Computing, National University of Singapore

  26. Base Models Can Reason By Taking a Cue From Training Data

    Oct 5, 2026Sophie L. Wang, Amil Dravid, Rulin Shao +3LLM PromptingRL for Language Model Reasoning

    MIT · UC Berkeley · University of Washington +1

  27. Errors of LLM-Assisted Literature Retrieval in Environmental Science: A Comparison Study of Abstract versus Full-text Based Prompts

    Oct 5, 2026Yanjun Chen, Yongfeng Zhang, Lanjing ZhangLLM PromptingLLM Reliability

    Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA. · East Brunswick High School, East Brunswick, NJ, USA. · Department of Computer Science, Rutgers University, Piscataway, NJ, USA. +3

  28. Author Representation Strategies for Zero-Shot Authorship Attribution: A Comparative Study of LLM-Based and Embedding-Based Approaches

    Oct 2, 2026Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi +2Authorship AttributionRepresentation Learning

    Luleå University of Technology, Sweden

  29. Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting

    Oct 1, 2026Ritika Rekhi, Bing Zhang, Md Arman Islam +7LLM Code GenerationCode Generation

    University at Buffalo, Buffalo, NY, USA · National Center for Supercomputing Applications, Urbana, IL, USA · IBM Research, Yorktown Heights, NY, USA

  30. OPSRD: On-Policy Self-Role Distillation

    Sep 30, 2026Weijie Ren, Yanwen Zhang, Hao Li +3LLM PromptingSelf-Distillation

    Zhejiang University · University of Electronic Science and Technology of China · University of Science and Technology of China