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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/1adrianb/lasp
공식 코드LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models
https://github.com/23japhone/halo
공식 코드HALO: Hierarchical Autonomous Logic-Oriented Orchestration for Multi-Agent LLM Systems
https://github.com/2sigmaedtech/llmasajudge
공식 코드https://github.com/3DAgentWorld/Toolkit-for-Prompt-Compression
공식 코드An Empirical Study on Prompt Compression for Large Language Models
https://github.com/4ai/ls-llama
공식 코드https://github.com/934865517zjk/csim
공식 코드https://github.com/AFeng-x/Draw-and-Understand
공식 코드Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
https://github.com/AICPS/LLM_4_PLC
공식 코드https://github.com/Aipura/LLM-ARK
공식 코드Evaluating and Enhancing Large Language Models for Conversational Reasoning on Knowledge Graphs
https://github.com/AntonioLiu97/llmICL
공식 코드LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
https://github.com/Armaniii/WIBA
공식 코드WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining
https://github.com/Atharva-Gundawar/LLM-Modulo-prompts
공식 코드Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach
https://github.com/Aurora-slz/BEATS
공식 코드https://github.com/BohdanPetryshyn/code-llama-fim-fine-tuning
공식 코드Optimizing Large Language Models for OpenAPI Code Completion
https://github.com/BohdanPetryshyn/openapi-completion-benchmark
공식 코드Optimizing Large Language Models for OpenAPI Code Completion
https://github.com/Djaber1979/-Behavioral-Augmentation-of-UML-Class-Diagrams
공식 코드https://github.com/DokiHollin/Multi-LLM-Based-Agent-RGD
공식 코드RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance
https://github.com/EncryptedBinary/Meta_analysis
공식 코드Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis
https://github.com/FeiLiu36/LLM4MOEA
공식 코드Large Language Model for Multi-objective Evolutionary Optimization
https://github.com/FocoosAI/ShowOrTell
공식 코드Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation
https://github.com/FoundationVision/Groma
공식 코드Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
https://github.com/GMahlerTheTragic/civic
공식 코드Using LLMs to label medical papers according to the CIViC evidence model
https://github.com/GeneralCoder365/agreemate
공식 코드https://github.com/Hector1993prog/Spanish_PromptORE
공식 코드https://github.com/HelloWorldLTY/ARIEL
공식 코드Towards Artificial Intelligence Research Assistant for Expert-Involved Learning
https://github.com/HongzhiQ/SupervisedVsLLM-EfficacyEval
공식 코드https://github.com/IBM/AutoVP
공식 코드AutoVP: An Automated Visual Prompting Framework and Benchmark
https://github.com/IanSteenstra/llm-alcohol-counselor
공식 코드Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing
https://github.com/IbrahimAlAzhar/LimTopic
공식 코드https://github.com/ImTanay/LLM-Automatic-Ontology-Generation
공식 코드https://github.com/JindongGu/Awesome-Prompting-on-Vision-Language-Model
공식 코드A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
https://github.com/KMnP/vpt
공식 코드https://github.com/LinDuoming/LogEval
공식 코드LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis
https://github.com/MLD3/steerability
공식 코드A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs
https://github.com/ManifoldRG/MultiNet
공식 코드Benchmarking Vision, Language, & Action Models on Robotic Learning Tasks
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