https://github.com/sergiotasconmorales/locvqallm
공식 코드Targeted Visual Prompting for Medical Visual Question Answering
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.
Targeted Visual Prompting for Medical Visual Question Answering
Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification
Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
Self-Pluralising Culture Alignment for Large Language Models
Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models
Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
Consistency-guided Prompt Learning for Vision-Language Models
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Test-Time Training on Nearest Neighbors for Large Language Models
Brevity is the soul of sustainability: Characterizing LLM response lengths
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
Towards Interpretable Mental Health Analysis with Large Language Models
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
What Do You Want? User-centric Prompt Generation for Text-to-image Synthesis via Multi-turn Guidance
Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions
TagGPT: Large Language Models are Zero-shot Multimodal Taggers
Adaptations of AI models for querying the LandMatrix database in natural language
Visual Instruction Inversion: Image Editing via Visual Prompting
LLMs can be Dangerous Reasoners: Analyzing-based Jailbreak Attack on Large Language Models
Large Language Models for Power Scheduling: A User-Centric Approach
Retrieval Augmented Generation based Large Language Models for Causality Mining
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.