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/cathyshyr/rare_disease_phenotype_extraction
공식 코드Identifying and Extracting Rare Disease Phenotypes with Large Language Models
https://github.com/causal-machine-learning-lab/awesome-causal-llm
공식 코드https://github.com/cbwbuaa/On-the-Worst-Prompt-Performance-of-LLMs
공식 코드https://github.com/changdaeoh/blackvip
공식 코드BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
https://github.com/chanliang/conner
공식 코드Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
https://github.com/chatgpt4pcg/chatgpt4pcg.github.io
공식 코드ChatGPT4PCG Competition: Character-like Level Generation for Science Birds
https://github.com/cheliu-computation/merl
공식 코드Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
https://github.com/chenshihfang/GOT
공식 코드https://github.com/chrismun/vvllm
공식 코드LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation
https://github.com/chrissyinreallife/kneesegmentwithsam
공식 코드Segmentation by registration-enabled SAM prompt engineering using five reference images
https://github.com/chuanmeng/qpp-genre
공식 코드Can We Use Large Language Models to Fill Relevance Judgment Holes?
https://github.com/clement-bonnet/text-to-pose
공식 코드From Text to Pose to Image: Improving Diffusion Model Control and Quality
https://github.com/cloudygoose/fewshot_lama
공식 코드An Empirical Study on Few-shot Knowledge Probing for Pretrained Language Models
https://github.com/cmubig/ccub
공식 코드https://github.com/codium-ai/alphacodium
공식 코드Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
https://github.com/colindoyle0000/llms-as-method-actors
공식 코드LLMs as Method Actors: A Model for Prompt Engineering and Architecture
https://github.com/compvis/latent-diffusion
공식 코드Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models
https://github.com/cr-gjx/suspicion-agent
공식 코드Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4
https://github.com/crockwell/cap3d
공식 코드https://github.com/cs-holder/DPDP
공식 코드Planning Like Human: A Dual-process Framework for Dialogue Planning
https://github.com/csbl-br/awesome-compbio-chatgpt
공식 코드Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in Computational Biology
https://github.com/cvi-szu/qa-clims
공식 코드Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
https://github.com/dahlia-chehata/rumoureval-with-vllms
공식 코드https://github.com/danielabd/apollo-cap
공식 코드Apollo: Zero-shot MultiModal Reasoning with Multiple Experts
https://github.com/data-dream-gdsp/Multiplex-CoT
공식 코드https://github.com/dcu-nlg/dcu-nlg-pbn
공식 코드https://github.com/deepmind/arnheim
공식 코드https://github.com/dennishgj/stf_acsr
공식 코드https://github.com/devichand579/HPT
공식 코드https://github.com/devika-prog/caregiver-conversational-support-tool
공식 코드https://github.com/diamond264/ByMyEyes
공식 코드By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting
https://github.com/dilab-kaist/erbench
공식 코드https://github.com/dlr-sc/style-vectors-for-steering-llms
공식 코드https://github.com/dodoxxb/lamper
공식 코드LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
https://github.com/dsaatusu/sentimentgpt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.