Papers Prompt Engineering
“Prompt Engineering” 태그가 달린 논문 1,874편 · 필터 해제
Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support
Background: Emergency Department (ED) return visits are commonly reviewed for quality assurance, but are often limited (e.g., to revisits within 48-72 hours) to increase actionable finding yield while minimizing chart re…
Prompt EngineeringUnifying Conformal Language Tasks with In-Context Ensembles
Many NLP tasks, such as summarization and extractive question answering, reduce to retrieving relevant content from documents under two constraints: coverage, retaining enough pertinent information to achieve some goal, …
Question AnsweringPrompt EngineeringStratified Consistency Distillation for Natural Language Formalization
Neurosymbolic reasoning has shown promising success in addressing complex reasoning tasks by combining large language models (LLMs) and symbolic solvers. While this approach shows promise, a fundamental challenge remains…
Prompt EngineeringThe Differential Reasoning Router: Operationalizing Cost-Aware LLM Annotation in E-commerce
Large Language Models (LLMs) are increasingly used to annotate structured product data in e-commerce, but early deployment often begins as a cold-start problem: only limited pre-launch labels are available, the value of …
Prompt EngineeringThomson: Continual Learning of Frontier Models for SovereignAI
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetry between developers and the diverse use…
Continual LearningPrompt EngineeringDomain AdaptationCRAMER: Control via Request-Aware Masking for Editing Recommenders
Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, exist…
Sequential RecommendationPrompt EngineeringGraph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering…
Prompt EngineeringAir Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation
Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic management have been semi-automated. In this article, we experimentally evaluate …
Semantic SimilarityPrompt EngineeringConcept Guidance: Precise, Training-Free Latent Control for Text-to-Image Generation
Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.g., for precisely controll…
Text-to-Image GenerationPrompt EngineeringMARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination
We present Multi-Agent Reasoning and Coordination (MARC), an open-source framework that replaces monolithic LLM prompting with deterministic multi-agent orchestration for clinical reasoning. MARC coordinates role-special…
Prompt EngineeringAnswer GenerationVibe Compiler: A Research-Logic Synthesis Tool That Runs without Prompt Engineering -Toward Enhancing Metacognition for Sustaining Agency in the Age of Generative AI-
Used as a capable servant, generative AI has greatly accelerated intellectual work, yet it also risks eroding human epistemic agency by encouraging uncritical acceptance of AI-generated reasoning. Preserving that agency …
Prompt EngineeringPrompt-Induced Waste in Coding Agents: Reasoning, Effort, Harness Design, and End-to-End Cost
Coding-agent efficiency cannot be characterized by token count or model price alone. We study how end-to-end cost and task success depend jointly on prompt semantics, inference effort, harness policy, model, task difficu…
Prompt EngineeringMDTD-ArtIR: Benchmarking Image Editing and Restoration Models for Art Image Restoration under Texture-Overlay Degradations
Restoring severely degraded visual media still remains a formidable challenge, as existing methods often hallucinate unnatural textures and contents, struggle with preserving color and texture, or fail to leverage partia…
Prompt EngineeringImage RestorationImage EditingWhat makes prompts a graph: necessary and sufficient conditions for prompt graph engineering
Prompts stopped being isolated strings some time ago. In real systems, one model call feeds another, retrieval interleaves with generation, routers branch, and aggregators merge parallel results. Practice converged on a …
Prompt EngineeringDual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives
Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering. However, existing prompt inversion me…
Prompt EngineeringImage EditingVisual prompt engineering for video models
In the age of foundation models, a model is only as good as its prompt. For this reason, prompt engineering has become an essential technique for improving language model performance. Since video models are currently bec…
Prompt EngineeringVisual ReasoningImage EditingThe Illusion of Secure LLM Code: Closing the Security Gap via Iterative Reprompting
Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authentication code remains uncertain. This paper evaluates the security arc…
Prompt EngineeringTransformer-Assisted LLM-Based Source Code Summarisation: to Enable More Secure Software Development
Neural Source Code Summarisation (NSCS) aims to generate natural language summaries of source code to improve developers' and maintainers' understanding of code. Source code summaries are vital during the maintenance pha…
Prompt EngineeringIt's Not What You Say, It's How You Say It: Evaluating LLM Responses to Expressions of Belief
Users frequently express their beliefs to large language models (LLMs). In some situations, the LLM should accept these contextual beliefs as true. In others, they should stick to their prior knowledge. Notably, users' e…
Prompt EngineeringA Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
Clinical notes contain many of the signs and symptoms that bring patients to care, yet this information rarely reaches structured fields. Existing extraction approaches either rely on context-insensitive rules that gener…
Prompt Engineering