{"total":27,"rows":[{"id":"2409.15452","title":"CUTE: Measuring LLMs' Understanding of Their Tokens","created":"2024-09-23","cited_by_count":31,"area":null},{"id":"2403.06949","title":"Materials science in the era of large language models: a perspective","created":"2024-03-11","cited_by_count":24,"area":null},{"id":"2409.08775","title":"What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use","created":"2024-09-13","cited_by_count":24,"area":null},{"id":"2405.14383","title":"Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering","created":"2024-05-23","cited_by_count":20,"area":null},{"id":"2406.18365","title":"Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability","created":"2024-06-26","cited_by_count":19,"area":null},{"id":"2406.19712","title":"Uncertainty Quantification in Large Language Models Through Convex Hull Analysis","created":"2024-06-28","cited_by_count":13,"area":null},{"id":"2504.07157","title":"GAAPO: Genetic Algorithmic Applied to Prompt Optimization","created":"2025-04-09","cited_by_count":11,"area":null},{"id":"2401.12097","title":"An Empirical Study of In-context Learning in LLMs for Machine Translation","created":"2024-01-22","cited_by_count":9,"area":null},{"id":"2503.01163","title":"Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers","created":"2025-03-03","cited_by_count":9,"area":null},{"id":"2411.10129","title":"Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation","created":"2024-11-15","cited_by_count":7,"area":null},{"id":"2504.13475","title":"LLM Sensitivity Evaluation Framework for Clinical Diagnosis","created":"2025-04-18","cited_by_count":7,"area":null},{"id":"2506.06950","title":"What Makes a Good Natural Language Prompt?","created":"2025-06-07","cited_by_count":7,"area":null},{"id":"2406.08183","title":"Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction","created":"2024-06-12","cited_by_count":6,"area":null},{"id":"2502.03804","title":"Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions","created":"2025-02-06","cited_by_count":6,"area":null},{"id":"2506.11561","title":"Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study","created":"2025-06-13","cited_by_count":2,"area":null},{"id":"2404.01582","title":"BERT-Enhanced Retrieval Tool for Homework Plagiarism Detection System","created":"2024-04-01","cited_by_count":1,"area":null},{"id":"2411.08932","title":"PyGen: A Collaborative Human-AI Approach to Python Package Creation","created":"2024-11-13","cited_by_count":1,"area":null},{"id":"2507.03829","title":"RELRaE: LLM-Based Relationship Extraction, Labelling, Refinement, and Evaluation","created":"2025-07-04","cited_by_count":1,"area":null},{"id":"2606.22974","title":"When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models","created":"2026-06-22","cited_by_count":1,"area":null},{"id":"2409.02711","title":"Creating a Gen-AI based Track and Trace Assistant MVP (SuperTracy) for PostNL","created":"2024-09-04","cited_by_count":0,"area":null},{"id":"2409.13928","title":"Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation","created":"2024-09-20","cited_by_count":0,"area":null},{"id":"2504.09860","title":"SUMART: SUMmARizing Translation from Wordy to Concise Expression","created":"2025-04-14","cited_by_count":0,"area":null},{"id":"2509.18846","title":"Model selection meets clinical semantics: Optimizing ICD-10-CM prediction via LLM-as-Judge evaluation, redundancy-aware sampling, and section-aware fine-tuning","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2601.13300","title":"OI-Bench: An Option Injection Benchmark for Evaluating LLM Susceptibility to Directive Interference","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2602.06692","title":"Evaluating Prompt Engineering Strategies for Sentiment Control in AI-Generated Texts","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2603.10477","title":"PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2608.11513","title":"Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation","created":"2026-08-11","cited_by_count":0,"area":null}],"next_cursor":null}