https://github.com/jincan333/lor-vp
공식 코드LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions
CogBench: a large language model walks into a psychology lab
Concept-Level Explainability for Auditing & Steering LLM Responses
BitBypass: A New Direction in Jailbreaking Aligned Large Language Models with Bitstream Camouflage
Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions
Incubating Text Classifiers Following User Instruction with Nothing but LLM
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models
DrLLM: Prompt-Enhanced Distributed Denial-of-Service Resistance Method with Large Language Models
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models
Efficient Black-Box Adversarial Attacks on Neural Text Detectors
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
Exploiting Prefix-Tree in Structured Output Interfaces for Enhancing Jailbreak Attacking
V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions
Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies
Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric Learning
Likelihood as a Performance Gauge for Retrieval-Augmented Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.