https://github.com/peiyance/revolve
공식 코드Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization
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.
Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization
PaCE: Parsimonious Concept Engineering for Large Language Models
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
PICBench: Benchmarking LLMs for Photonic Integrated Circuits Design
AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked
Wordflow: Social Prompt Engineering for Large Language Models
GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers
Ask, and it shall be given: On the Turing completeness of prompting
MoLEx: Mixture of Layer Experts for Finetuning with Sparse Upcycling
GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation
TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation
RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models
Leveraging Large Language Model to Generate a Novel Metaheuristic Algorithm with CRISPE Framework
Bayesian Prompt Learning for Image-Language Model Generalization
CultureLLM: Incorporating Cultural Differences into Large Language Models
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.