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.

Do Deployment Constraints Make LLMs Hallucinate Citations? An Empirical Study across Four Models and Five Prompting Regimes
Mar 7, 2026Chen Zhao, Yuan Tang, Yitian QianHallucination in Language Models LLM Prompting
New York University, United States · Carnegie Mellon University, United States · Boston University, United States
Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
Mar 6, 2026Shubhangi Upasani, Chen Wu, Jay Rainton +4Inference-Time Adaptation of Language Models LLM Prompting
SambaNova Systems, Inc · Stanford University
Position: Vector Prompt Interfaces Should Be Exposed to Enable Customization of Large Language Models
Mar 4, 2026Liangwei Yang, Shiyu Wang, Haolin Chen +12LLM Prompting Prompt Tuning
Salesforce AI Research, Palo Alto, CA, United States.
The Arrival of AGI? When Expert Personas Exceed Expert Benchmarks
Mar 4, 2026Drake Mullens, Stella ShenLLM Prompting Language Model Generation Evaluation
Tarleton State University · University of Texas at Tyler
T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning
Mar 4, 2026Qinsi Wang, Hancheng Ye, Jinhee Kim +12Structured Reasoning LLM Reasoning
Duke University · UT Austin · Meta
Evaluating Prompting Strategies for Chart Question Answering with Large Language Models
Mar 3, 2026Ruthuparna Naikar, Ying ZhuLLM Prompting Chart QA
Georgia State University, Atlanta GA, USA
Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrieval
Mar 2, 2026Varun KotteQuery Reformulation Dense Retrieval
Adobe
Natural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift
Feb 26, 2026Hyunwoo Kim, Hanau Yi, Jaehee Bae +1LLM Prompting Prompt Engineering
ddai Inc.
Scaling In, Not Up? Testing Thick Citation Context Analysis with GPT-5 and Fragile Prompts
Feb 25, 2026Arno SimonsScientific Communication Prompt Sensitivity
Technische Universität Berlin
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference
Feb 24, 2026Cuong Chi Le, Minh V. T Pham, Tung Vu Duy +4LLM Self-Refinement LLM Prompting
The University of Texas at Dallas · FPT Software AI Center · VinUniversity +1
Examining and Addressing Barriers to Diversity in LLM-Generated Ideas
Feb 23, 2026Yuting Deng, Melanie Brucks, Olivier ToubiaDiverse Text Generation LLM Prompting
Columbia Business School
Structured Prompt Language: Declarative Context Management for LLMs
Feb 23, 2026Wen G. GongLLM Prompting LLM Context Management
How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding
Feb 23, 2026Wei Chen, Guoyang Ju, Yuanyuan QiLLM Input Optimization LLM Prompting
China Jiliang University, Hangzhou, China
Exploring LLMs for User Story Extraction from Mockups
Feb 19, 2026Diego Firmenich, Leandro Antonelli, Bruno Pazos +2Requirements Engineering Text Generation
Departamento de Informática, Facultad de Ingeniería, Universidad Nacional de la Patagonia, Argentina · LIFIA, Facultad de Informática, Universidad Nacional de La Plata, Argentina · CAETI, Facultad de Tecnología Informática - Universidad Abierta Interamericana +3
Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
Feb 18, 2026Felix Fricke, Simon Malberg, Georg GrohInference-Time Optimization Tree of Thoughts
School of Computation, Information and Technology Technical University of Munich
Transforming GenAI Policy to Prompting Instruction: An RCT of Scalable Prompting Interventions in a CS1 Course
Feb 17, 2026Ruiwei Xiao, Runlong Ye, Xinying Hou +4Generative AI in Education AI Literacy
Carnegie Mellon University, Pittsburgh, PA, United States · University of Toronto, Toronto, ON, Canada · University of Michigan, Ann Arbor, MI, United States +1
LLMStructBench: Benchmarking Large Language Model Structured Data Extraction
Feb 16, 2026Sönke Tenckhoff, Mario Koddenbrock, Erik RodnerLLM Information Extraction Document Parsing Benchmarks
KI-Werkstatt / FB2, University of Applied Sciences Berlin
Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought
Feb 16, 2026Yuling Jiao, Yanming Lai, Huazhen Lin +3LLM Prompting In-Context Learning
School of Artificial Intelligence, Wuhan University · National Center for Applied Mathematics in Hubei, Wuhan University · Hubei Key Laboratory of Computational Science, Wuhan University +6
Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know"
Feb 4, 2026Dhruv Madhwal, Lyuxin David Zhang, Dan Roth +2LLM Prompting Question Decomposition
Arizona State University · University of Pennsylvania · Oracle AI
Measuring Pragmatic Influence in Large Language Model Instructions
Feb 2, 2026Yilin Geng, Omri Abend, Eduard Hovy +1LLM Evaluation LLM Prompting
University of Melbourne · Hebrew University of Jerusalem
Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs
Jan 5, 2026Amirali Ebrahimzadeh, Seyyed M. SaliliLong-Context Retrieval LLM Hallucination Mitigation
Department of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA · Independent Researcher, Ann Arbor, MI 48106, USA
Rethinking On-policy Optimization for Query Augmentation
Oct 20, 2025Zhichao Xu, Shengyao Zhuang, Xueguang Ma +6RL for Language Models LLM Prompting
University of Utah · 2The University of Queensland · University of Waterloo +5
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Oct 1, 2025Jiayi Zhang, Simon Yu, Derek Chong +4LLM Prompting Model Collapse
Northeastern University · Stanford University · West Virginia University
Towards AI-Assisted Research Writing: Benchmarking LLMs for AI/ML Introduction Generation
Aug 19, 2025Krishna Garg, Firoz Shaik, Sambaran Bandyopadhyay +1LLM Evaluation Language Model Generation Evaluation
University of Illinois Chicago · Adobe Research
Scaling Personality Control in LLMs with Big Five Scaler Prompts
Aug 8, 2025Gunhee Cho, Yun-Gyung CheongPersona-Conditioned Generation Controllable Text Generation
Sungkyunkwan University, South Korea
Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks
Aug 6, 2025Sangwon Hyun, Hyunjun Kim, Jinhyuk Jang +2LLM Prompting Code Generation
Adelaide University, Adelaide, SA, Australia · Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
Aug 1, 2025Zizhan Ma, Wenxuan Wang, Meidan Ding +7LLM Prompting RL for Language Model Reasoning
The Chinese University of Hong Kong · Renmin University of China · Shenzhen University +3
Introducing HALC: A general pipeline for the systematic and reliable construction of prompts for automated coding with LLMs in the computational social sciences
Jul 29, 2025Andreas Reich, Claudia Thoms, Tobias SchrimpfLLM Prompting LLM-Assisted Annotation
Structured Prompting and Automated Evaluation in Fixed Synthetic Japanese-Language Counseling Dialogues
Jun 28, 2025Keita Kiuchi, Yoshikazu Fujimoto, Hideyuki Goto +5Mental Health Counseling LLM-as-a-Judge
Japan National Institute of Occupational Safety and Health, Kanagawa, Japan · Kaze To Taiyo, Tokyo, Japan · Saga Occupational Health Association, Saga, Japan +5
$π$ -CoT: Prolog-Initialized Chain-of-Thought Prompting for Multi-Hop Question-Answering
Jun 25, 2025Chao Wan, Albert Gong, Mihir Mishra +3Multi-Hop QA LLM Prompting
Cornell University.

Mar 7, 2026J/K move · Enter open · S save

Do Deployment Constraints Make LLMs Hallucinate Citations? An Empirical Study across Four Models and Five Prompting Regimes

Chen Zhao, Yuan Tang, Yitian Qian

New York University, United States · Carnegie Mellon University, United States · Boston University, United States

Hallucination in Language Models LLM Prompting Citation Verification LLM Hallucination Detection

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LLMs are increasingly used to draft academic text and to support software engineering (SE) evidence synthesis, but they often hallucinate bibliographic references that look legitimate. We study how deployment-motivated prompting constraints affect citation verifiability in a closed-book setting. Using 144 claims (24 in SE&CS) and a deterministic verification pipeline (Crossref + Semantic Scholar), we evaluate two proprietary models (Claude Sonnet, GPT-4o) and two open-weight models (LLaMA~~3.1-8B, Qwen~~2.5-14B) across five regimes: Baseline, Temporal (publication-year window), Survey-style breadth, Non-Disclosure policy, and their combination. Across 17,443 generated citations, no model exceeds a citation-level existence rate of 0.475; Temporal and Combo conditions produce the steepest drops while outputs remain format-compliant (well-formed bibliographic fields). Unresolved outcomes dominate (36-61%); a 100-citation audit indicates that a substantial fraction of Unresolved cases are fabricated. Results motivate post-hoc citation verification before LLM outputs enter SE literature reviews or tooling pipelines.