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Prompt Engineering
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Prompt engineering is the process of designing and refining the prompts used to generate text from language models, such as GPT-3 or similar models. The goal of prompt engineering is to improve the quality and relevance of the generated text by carefully crafting the prompts to elicit the desired responses from the model.
Prompt engineering involves several steps, including selecting the appropriate model architecture and parameters, designing the prompt format and structure, selecting the appropriate task and training data, and fine-tuning the model using the selected prompt and data.
Prompt engineering is a crucial step in the development of language models, as it can greatly influence the quality and effectiveness of the model's responses. By carefully designing and refining the prompts used to generate text, researchers and developers can improve the accuracy and relevance of the model's output, making it more useful for a wide range of applications, including chatbots, language translation, content creation, and more.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Emotional Support with LLM-based Empathetic Dialogue Generation
Leveraging Language Prior for Infrared Small Target Detection
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach
State and Memory is All You Need for Robust and Reliable AI Agents
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges
Prompt Engineering Techniques for Mitigating Cultural Bias Against Arabs and Muslims in Large Language Models: A Systematic Review
Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?
Automatic Large Language Models Creation of Interactive Learning Lessons
SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Optimizing Web-Based AI Query Retrieval with GPT Integration in LangChain A CoT-Enhanced Prompt Engineering Approach
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
Instruction Following by Boosting Attention of Large Language Models
Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs
Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
LLM-as-a-Fuzzy-Judge: Fine-Tuning Large Language Models as a Clinical Evaluation Judge with Fuzzy Logic
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling
Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
A Navigation Framework Utilizing Vision-Language Models
Transforming Expert Knowledge into Scalable Ontology via Large Language Models
Brevity is the soul of sustainability: Characterizing LLM response lengths
From Legal Texts to Defeasible Deontic Logic via LLMs: A Study in Automated Semantic Analysis
Technical Report for Argoverse2 Scenario Mining Challenges on Iterative Error Correction and Spatially-Aware Prompting
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
Prompt to Protection: A Comparative Study of Multimodal LLMs in Construction Hazard Recognition
Unable to Forget: Proactive lnterference Reveals Working Memory Limits in LLMs Beyond Context Length
Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation
DynamicMind: A Tri-Mode Thinking System for Large Language Models
Handle-based Mesh Deformation Guided By Vision Language Model
Towards Holistic Visual Quality Assessment of AI-Generated Videos: A LLM-Based Multi-Dimensional Evaluation Model
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