https://github.com/xk-huang/OrdinalCLIP
공식 코드OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
Iterative Forward Tuning Boosts In-Context Learning in Language Models
Supervised Knowledge Makes Large Language Models Better In-context Learners
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
SAMAug: Point Prompt Augmentation for Segment Anything Model
EntGPT: Linking Generative Large Language Models with Knowledge Bases
Control Illusion: The Failure of Instruction Hierarchies in Large Language Models
PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
A Survey on the Optimization of Large Language Model-based Agents
Attention Prompting on Image for Large Vision-Language Models
Exploring the Capabilities of Large Multimodal Models on Dense Text
MMRL: Multi-Modal Representation Learning for Vision-Language Models
Simulating Opinion Dynamics with Networks of LLM-based Agents
Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration
AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting
ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis
Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models
ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.