Few-Shot Prompting

Follow this topic Ask about this topic

Momentum

3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 34

All topics

How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs
Sep 28, 2026Ali Alfageeh, Rahul Gopinath, Amin AlipourLLM Prompting Few-Shot Prompting
University of Houston · The University of Sydney
Language Specificity vs. Domain Diversity: Benchmarking Transformers for Bangla Medical NER
Sep 24, 2026Rakib Abdullah, Md. Maruful Islam MarufNamed Entity Recognition Multilingual Language Model Evaluation
Department of Artificial Intelligence and Data Science Green University of Bangladesh Dhaka, Bangladesh
ClusterFewshot: Improving Few-shot Optimization for LLMs workflow
Sep 22, 2026Omri Bar Haim, Shahar Katz, Lior WolfIn-Context Example Selection Large Language Model-Guided Optimization
Blavatnik School of Computer Science, Tel Aviv University
IDRAAK: From Multi-Agent NLP to Few-Shot Prompting for Semantic Drift Detection in Technical Requirements
Aug 9, 2026Shiva AhirRequirements Engineering Few-Shot Prompting
Department of Electrical & Computer Engineering Stony Brook University New York, United States of America
Example-Guided Prompting for Document-Level Text Simplification
Aug 5, 2026Marina Litvak, Ariel Perstin, Ilan Shtilman +1LLM Prompting Few-Shot Prompting
SCE, Beer Sheva, Israel · ScaDS.AI, TUD · Dresden, Germany
Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve
Aug 4, 2026Denys Pushkin, Albert Q. Jiang, Aryo Lotfi +2LLM Prompting CoT Reasoning
EPFL · Apple · Mistral AI
FLARE: Few-shot Learning-based Adaptive Reflective Engine
Aug 3, 2026Dhanasekar Sundararaman, Bharat Gandhi, Aashna Garg +1LLM Prompting Large Language Model-Guided Optimization
Microsoft Redmond, WA, USA
Few-Shot Concept Prompt Learning for Segmentation Foundation Models via Visual Grounding
Aug 3, 2026Rahul Venkataramani, Rachana SathishMedical Imaging Foundation Models Promptable Image Segmentation
Advanced Technology Group, GE HealthCare
What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification
Jul 20, 2026Guosheng Li, Fenghui Ren, Bin Liu +5LLM Fine-Tuning Few-Shot Prompting
School of Computing and Information Technology, University of Wollongong · School of Business, University of Wollongong · Business School, University of Sydney +1
Neuron-Aware Active Few-Shot Learning for LLMs
Jul 2, 2026Zhuowei Chen, Liwei Chen, Christian Schunn +2Active Learning Few-Shot Learning
University of Pittsburgh, USA
Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners
Jun 30, 2026Yunhan Wang, Eshika Khandelwal, Edson Araujo +3Multimodal Large Language Models Few-Shot Prompting
Tuebingen AI Center, University of Tuebingen, Germany
Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News
Jun 25, 2026Raven Adam, David Maier, Marie KoglerText Classification Few-Shot Prompting
University of Graz, Austria · Technical University of Graz, Austria
Automatic Generation of Highlights for Academic Paper Via Prompt-based Learning
Jun 24, 2026Yi Xiang, Chengzhi Zhang, Heng ZhangPrompt Learning Text Summarization
Department of Information Management, Nanjing University of Science and Technology, Nanjing, China
Concept-Constrained Prompt Learning for Few-Shot CLIP Adaptation
Jun 21, 2026Na Sang, Ding Ma, Rui Sang +1Prompt Learning VLM Adaptation
University of California, San Diego, La Jolla, CA, USA · Georgia Institute of Technology, Atlanta, GA, USA · Independent Researcher
ttda704 at SemEval-2026 Task 6: Structured Chain-of-Thought Prompting for Political Evasion Detection
Jun 14, 2026Tai Tran Tan, An Dinh ThienCoT Reasoning Pragmatic Reasoning in Language Models
University of Information Technology, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam
Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?
Jun 13, 2026Jakob Mraz, Tomaž Curk, Blaž ZupanRelation Extraction Few-Shot Learning
University of Ljubljana, Ljubljana, Slovenia · Baylor College of Medicine, Houston, Texas, USA
Robust Active Learning for Few-Shot Example Selection in Text-to-SQL
Jun 8, 2026Arash PourhabibSubmodular Optimization LLM Grounding
NVIDIA
Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning
Jun 7, 2026Bernhard Kneip, Nhien-An Le-Khac, Hong-Hanh Nguyen-LeExplainable Artificial Intelligence CoT Reasoning
Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
Jun 5, 2026Kaouther Mouheb, Amos Pomp, Antoine Manenti +9Few-Shot Prompting Clinical Information Extraction
Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands · Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands · Department of Electrical and Electronics Engineering, ENSEEIHT, Toulouse, France +3
Temporal Stability and Few-Shot Prompting in Math Task Assessment
May 28, 2026Danielle S. Fox, Brenda L. Robles, Elizabeth DiPietro Brovey +1Generative AI in Education Few-Shot Prompting
Learning Research and Development Center, University of Pittsburgh · Institute for Learning, University of Pittsburgh
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
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
Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries
May 15, 2026Liqi Zhou, Jiafu LiHealthcare Clinical Triage
Locale-Conditioned Few-Shot Prompting Mitigates Demonstration Regurgitation in On-Device PII Substitution with Small Language Models
May 13, 2026Anuj Sadani, Deepak KumarPrivacy-Preserving Language Models Few-Shot Prompting
Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA
May 5, 2026Ahmed Bajaber, Mohammed AlliheediMulti-Hop QA LLM Evaluation
1Saudi Med AI Lab (SMAIL) 2Prince Sultan University, Riyadh, Saudi Arabia · 2Prince Sultan University, Riyadh, Saudi Arabia · 3Al-Baha University, Al Baha, Saudi Arabia
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
Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models
Apr 22, 2026Sachin KumarSmall Language Models Few-Shot Prompting
LexisNexis, USA
SwanNLP at SemEval-2026 Task 5: An LLM-based Framework for Plausibility Scoring in Narrative Word Sense Disambiguation
Apr 17, 2026Deshan Sumanathilaka, Nicholas Micallef, Julian Hough +1LLM Evaluation Few-Shot Prompting
Department of Computer Science, Swansea University, Wales, UK
Prompt-Driven Code Summarization: A Systematic Literature Review
Apr 16, 2026Afia Farjana, Zaiyu Cheng, Antonio MastropaoloLLM Evaluation LLM Prompting
William & Mary, USA
DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection
Mar 24, 2026Gautam Rajendrakumar Gare, Neehar Peri, Matvei Popov +3Large Vision-Language Models Few-Shot Object Detection
Carnegie Mellon University · Roboflow
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
Dec 17, 2025Gangxiong Zhang, Yongchao Long, Yuxi Zhou +2Algorithmic Fairness Few-Shot Prompting
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China · Institute for Artificial Intelligence, Peking University, Beijing, China · DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China +1
Training-free LLM Verification via Recycling Few-shot Examples
Jun 8, 2025Dongseok Lee, Jimyung Hong, Dongyoung Kim +1LLM Answer Verification Few-Shot Prompting
Yonsei University · KAIST
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Mar 7, 2024Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5Healthcare Medical Diagnosis
2Indian Institute of Technology Bombay · 1IBM Research
Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization
Date pendingAyan Majumdar, Shounak Paul, Pushpdeep Singh +6VLM Evaluation LLM Prompting
MPI-SWS, Germany · Saarland University, Germany · INRIA, France

Sep 28, 2026, cs.SEJ/K move · Enter open · S save

How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs

Ali Alfageeh, Rahul Gopinath, Amin Alipour

University of Houston · The University of Sydney

LLM Prompting Few-Shot Prompting LLM Reliability Prompt Optimization Prompt Compression

Open paper AskPDF arXiv

Prompts are the primary mechanism for directing the behavior of large language models (LLMs). Yet the internal structure and causal hierarchy of prompts remain poorly understood: which parts are causally necessary and which are redundant is an open question. This opacity can have severe consequences. Subtle prompt variations can silently shift model outputs in critical software systems, and engineers lack techniques to reason about prompt reliability. We present \framework, a blackbox prompt-minimization framework that reduces few-shot prompts to their necessary minimal subset. We use a case study to apply \framework to a few-shot learning system and demonstrate the insights that this framework can provide. Our experiments show that few-shot exemplars can be reduced by a mean of 65.3%~

\pm

~15.8% in character count while fully preserving propositional output fidelity. The models preferentially retain logical identifiers and constraint declarations while discarding natural language prose and cross-prompt relational annotations. Our analysis also shows that some models are universal encoders, able to produce highly legible yet minimized prompts, while others are universal decoders, able to interpret minimized prompts from most other models. By identifying which components are indispensable, \framework provides a principled basis for prompt compression and structural analysis of few-shot exemplars.

Momentum

Related topics

Latest papers 34