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/hazyresearch/ama_prompting
공식 코드Ask Me Anything: A simple strategy for prompting language models
https://github.com/healthx-lab/biomedcoop
공식 코드BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
https://github.com/heaven-zhw/llm-on-tabfact
공식 코드https://github.com/henryyantq/optimallm
공식 코드https://github.com/hiroki39/noisy-exemplars-make-large-language-models-more-robust
공식 코드Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral Analysis
https://github.com/hjbahng/visual_prompting
공식 코드https://github.com/hkust-knowcomp/constraintchecker
공식 코드ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases
https://github.com/hkust-zhiyao/rtllm
공식 코드RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model
https://github.com/hlr/misinformation-detection
공식 코드Using Persuasive Writing Strategies to Explain and Detect Health Misinformation
https://github.com/hltchkust/chatgpt-evaluation
공식 코드https://github.com/holarissun/prompt-oirl
공식 코드Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
https://github.com/hsb1357173526/dynamic_visual_prompting
공식 코드Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting
https://github.com/huangd1999/AgentCoder
공식 코드AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
https://github.com/hulianyuyy/comma
공식 코드https://github.com/iaar-shanghai/ctgsurvey
공식 코드Controllable Text Generation for Large Language Models: A Survey
https://github.com/ianarawjo/ChainForge
공식 코드ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing
https://github.com/ibm/vp-llr
공식 코드https://github.com/icetlab/codeprompteval
공식 코드The Impact of Prompt Programming on Function-Level Code Generation
https://github.com/ihealthlab/dietitianexameval
공식 코드https://github.com/ink-usc/link
공식 코드https://github.com/inst-it/inst-it
공식 코드Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning
https://github.com/intel/TVP
공식 코드Text-Visual Prompting for Efficient 2D Temporal Video Grounding
https://github.com/intellabs/multimodal_cognitive_ai
공식 코드NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation
https://github.com/ioannis-papadimitriou/rag-playground
공식 코드https://github.com/iodmitri/gradelab
공식 코드Grade Score: Quantifying LLM Performance in Option Selection
https://github.com/ipouyall/can-llms-be-lateral-thinkers
공식 코드uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?
https://github.com/irgroup/sigir2025-contrastive_prompting
공식 코드Evaluating Contrastive Feedback for Effective User Simulations
https://github.com/isyufeng/FactFinders
공식 코드https://github.com/jacobsonradical/llm-dual-trait
공식 코드Dual Traits in Probabilistic Reasoning of Large Language Models
https://github.com/jacobyhsi/InterpreTabNet
공식 코드InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
https://github.com/jakec007/automated_gkc-ci_privacy_policy_annotations
공식 코드https://github.com/jasonppy/promptingwhisper
공식 코드Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization
https://github.com/jessicaxtang/exercisellm
공식 코드https://github.com/jiaweixu98/llm-ta
공식 코드https://github.com/jieshibo/memvp
공식 코드Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.