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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Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French
Apr 26, 2026Ido Dahan, Omer Toledano, Roey J. Gafter +4LLM Prompting
Faculty of Computer and Information Science, Institute for Applied AI Research · Department of Hebrew Language and Sociolinguistics · Department of Politics and Government +1
Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models
Apr 25, 2026Gautam Kishore Shahi, Oliver HummelLLM Evaluation LLM Prompting
Technische Hochschule Mannheim, Germany
When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL2DL
Apr 25, 2026Yijiashun Qi, Xiang Xu, Yuxuan LiLLM Self-Correction Logical Reasoning
University of Michigan · ByteDance Inc. · University of Pennsylvania
CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning
Apr 25, 2026Shuxu Chen, Yitian Zhou, Jiaquan Zhang +6Adversarial Prompt Generation LLM Prompting
Department of Electronics and Information Convergence Engineering, Kyung Hee University, Republic of Korea · University of Electronic Science and Technology of China, Chengdu, China · East China Jiaotong University, Nanchang, China +1
Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt
Apr 25, 2026Zhenzhen Huang, Chaoning Zhang, Fachrina Dewi Puspitasari +4LLM Prompting Prompt Optimization
School of Computer Science and Engineering, University of Electronic Science and Technology of China · Department of Electronic Engineering, Kyung Hee University
Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact
Apr 25, 2026Kiyoshige Garces, Gloria Milena Fernandez-Nieto, Linxuan Zhao +4LLM Inference Efficiency LLM Prompting
RMIT University · Monash University · Adelaide University +2
From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verification
Apr 24, 2026Md Erfan, Md Kamal Hossain Chowdhury, Ahmed Ryan +1LLM Prompting Code Generation
Department of Computer Science, The University of Alabama, Tuscaloosa, USA · Alabama Water Institute, The University of Alabama, Tuscaloosa, USA
Learning Evidence Highlighting for Frozen LLMs
Apr 24, 2026Shaoang Li, Yanhang Shi, Yufei Li +10Long-Context QA LLM Prompting
Stony Brook University · Meta AI
Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations
Apr 24, 2026Anna Arnaudo, Riccardo Coppola, Maurizio Morisio +4Requirements Engineering LLM Prompting
Department of Control and Computer Engineering, Politecnico di Torino · Politecnico di Torino
Voice Under Revision: Large Language Models and the Normalization of Personal Narrative
Apr 24, 2026Tom van NuenenLLM Prompting
Social Sciences D-Lab, University of California, Berkeley
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
When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation
Apr 23, 2026Anamta Khan, Ratna Kandala, Deepti +2LLM Prompting Automated Fact-Checking
University of Michigan · University of Kansas · IIT Jodhpur
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Apr 23, 2026Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin +6LLM Prompting Automated Feature Engineering
University of Oxford · Vela Research · Amazon
Unlocking the Power of Large Language Models for Multi-table Entity Matching
Apr 23, 2026Yingkai Tang, Taoyu Su, Wenyuan Zhang +2Entity Resolution LLM Prompting
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · School of Information Engineering China University of Geosciences Beijing, Beijing, China
Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
Apr 22, 2026Xuhong He, To Eun Kim, Maik Fröbe +3LLM Prompting Multilingual IR
Carnegie Mellon University · Pittsburgh, PA, USA · Friedrich-Schiller-Universität Jena +5
BODHI: Precise OS Kernel Specification Inference
Apr 22, 2026Zhiming Chang, Ziyang LiKnowledge Augmentation for Language Models LLM Prompting
Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore, US · Department of Computer Science Johns Hopkins University Baltimore, US
Can "AI" Be a Doctor? A Study of Empathy, Readability, and Alignment in Clinical LLMs
Apr 22, 2026Mariano Barone, Francesco Di Serio, Roberto Moio +4Healthcare LLM Prompting
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, Naples, 80125, Italy. · Department of Translational Medical Sciences, University of Campania ”Luigi Vanvitelli”, Via Leonardo Bianchi, Naples, 80131, Italy. · Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, 2309 Sheridan Rd, Evanston, 60201, IL, United States.
Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs
Apr 22, 2026Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani +2LLM Evaluation Educational Assessment
Leibniz Information Centre for Science and Technology (TIB) · University of Genoa
Benchmarking Large Language Models for Safety Data Extraction
Apr 22, 2026Jonas Grill, Thomas Bayer, Sören BerlingerLLM Evaluation LLM Prompting
1SAP SE, Germany. · Institute for Digital Transformation, Ravensburg-Weingarten University.
The signal is the ceiling: Measurement limits of LLM-predicted experience ratings from open-ended survey text
Apr 21, 2026Andrew Hong, Jason Potteiger, Luis E. ZapataLLM Evaluation LLM Prompting
Dimension Labs
Less Is More: Cognitive Load and the Single-Prompt Ceiling in LLM Mathematical Reasoning
Apr 20, 2026Manuel Israel CazaresPrompt Sensitivity LLM Prompting
Bytepro AI Mazatlán, Sinaloa, Mexico
Understanding the Prompt Sensitivity
Apr 20, 2026Yang Liu, Chenhui ChuPrompt Sensitivity LLM Prompting
Kyoto University
Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design
Apr 20, 2026Xinyao Zhang, Nicole Sonne Heckmann, Manuela Del Castillo Suero +2LLM Evaluation Healthcare
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
Semantic Density Effect (SDE): Maximizing Information Per Token Improves LLM Accuracy
Apr 19, 2026Amr AhmedLLM Prompting Prompt Optimization
Public Health Department, Riyadh First Health Cluster · Ministry of Health, Saudi Arabia
Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
Apr 19, 2026Elaf Alhazmi, Quan Z. Sheng, Wei Emma ZhangLLM Prompting In-Context Learning
School of Computing, Macquarie University, Australia · College of Engineering and Computing in Al-Lith, Umm Al-Qura University, Saudi Arabia · School of Computer and Mathematical Sciences, Adelaide University, Australia
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
Apr 18, 2026Wei-Chi Wu, Sheng-Lun Wei, Hen-Hsen Huang +1Multilingual Language Model Evaluation LLM 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
Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering
Apr 18, 2026Francesco Sovrano, Gabriele Dominici, Alberto BacchelliSoftware Engineering LLM Prompting
Collegium Helveticum at ETH Zurich, Switzerland · University of Zurich, Switzerland · University of Italian-speaking Switzerland, Switzerland
Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
Apr 17, 2026Nicolò Pagan, Christopher Barrie, Chris Andrew Bail +1LLM Prompting Social Bias in Language Models
University of Zurich, Department of Informatics, Zurich, CH · New York University, Department of Sociology, New York, USA · University of Oxford, Department of Sociology, Oxford, UK +2
Schema-Key Wording as an Instruction Channel in Structured Generation under Constrained Decoding
Apr 16, 2026Yifan LeConstrained Generative Modeling Constrained Decoding
Independent Researcher
StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation
Apr 16, 2026Geonhui Jang, Dongyoon Han, YoungJoon YooLLM Prompting LLM Code Generation
Dept. of Artificial Intelligence, Chung-Ang University · NAVER AI Lab

Apr 26, 2026, cs.CLJ/K move · Enter open · S save

Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French

Ido Dahan, Omer Toledano, Roey J. Gafter, Sharon Pardo+3

Faculty of Computer and Information Science, Institute for Applied AI Research · Department of Hebrew Language and Sociolinguistics · Department of Politics and Government+1

LLM Prompting

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Cross-Lingual Text Simplification (CLTS) aims to make content more accessible across languages by simultaneously addressing both linguistic complexity and translation. This study investigates the effectiveness of different prompting strategies for CLTS between English and French using large language models (LLMs). We examine five distinct prompting systems: a direct prompt instructing the LLM to perform both translation and simplification simultaneously, two Composition approaches that either translate-then-simplify or simplify-then-translate within a single prompt, and two decomposition approaches that perform the same operations in separate, consecutive prompts. These systems are evaluated across a diverse set of five corpora of different genres (Wikipedia and medical texts) using seven state-of-the-art LLMs. Output quality is assessed through a multi-faceted evaluation framework comprising automatic metrics, comprehensive linguistic feature analysis, and human evaluation of simplicity and meaning preservation. Our findings reveal that while direct prompting consistently achieves the highest BLEU scores, indicating meaning fidelity, Translate-then-Simplify approaches demonstrate the highest simplicity, as measured by the linguistic features.

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