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/mohammadJaliliTorkamani/Kajal
공식 코드Kajal: Extracting Grammar of a Source Code Using Large Language Models
https://github.com/montemac/activation_additions
공식 코드https://github.com/moymix/taskmatrix
공식 코드Low-code LLM: Graphical User Interface over Large Language Models
https://github.com/mqo00/rope
공식 코드What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use
https://github.com/msamogh/hypmix
공식 코드https://github.com/muzairkhattak/multimodal-prompt-learning
공식 코드https://github.com/muzairkhattak/promptsrc
공식 코드Self-regulating Prompts: Foundational Model Adaptation without Forgetting
https://github.com/muzairkhattak/protext
공식 코드Learning to Prompt with Text Only Supervision for Vision-Language Models
https://github.com/n28div/llm-disjointness
공식 코드Enriching Ontologies with Disjointness Axioms using Large Language Models
https://github.com/nafis-neehal/CTBench_LLM
공식 코드https://github.com/namazifard/multilingual-idioms-similes
공식 코드Comparative Study of Multilingual Idioms and Similes in Large Language Models
https://github.com/naver/bergen
공식 코드https://github.com/naylenv/uf-fgtg
공식 코드A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis
https://github.com/ncbi-nlp/llm-medicine-primer
공식 코드https://github.com/nec-research/sensitivity-consistency-LLM
공식 코드What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering
https://github.com/networks-learning/counterfactual-llms
공식 코드https://github.com/neucol/llm-conversion-performance
공식 코드https://github.com/nguyentthong/CLNTM
공식 코드Enhanced Short Text Modeling: Leveraging Large Language Models for Topic Refinement
https://github.com/nifangbaage/explicit-visual-prompt
공식 코드Explicit Visual Prompting for Low-Level Structure Segmentations
https://github.com/nkanak/brag-fake-news-campaigns
공식 코드https://github.com/nlp-tlp/chatgpt-fmc
공식 코드Large Language Models for Failure Mode Classification: An Investigation
https://github.com/nlp2ct/hil-mt
공식 코드Human-in-the-loop Machine Translation with Large Language Model
https://github.com/nvlabs/verilog-eval
공식 코드Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation
https://github.com/nyushcs/gaugllm
공식 코드GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
https://github.com/oele-isis-vanderbilt/AIED24_LBR
공식 코드Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis
https://github.com/olivesgatech/pointprompt
공식 코드Benchmarking Human and Automated Prompting in the Segment Anything Model
https://github.com/onekq/webapp1k
공식 코드Insights from Benchmarking Frontier Language Models on Web App Code Generation
https://github.com/open-social-world/autolibra
공식 코드https://github.com/openai/CLIP
공식 코드Learning Transferable Visual Models From Natural Language Supervision
https://github.com/openai/human-eval
공식 코드Safurai 001: New Qualitative Approach for Code LLM Evaluation
https://github.com/opengvlab/phygenbench
공식 코드Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation
https://github.com/optml-group/ilm-vp
공식 코드Understanding and Improving Visual Prompting: A Label-Mapping Perspective
https://github.com/oxai/gpt3-jobadvert-bias
공식 코드Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements
https://github.com/panagiotis-koletsis/kleptotracedataset
공식 코드Entity Extraction from High-Level Corruption Schemes via Large Language Models
https://github.com/parth166/m3g-clinicaldermatology
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.