https://github.com/dyabel/detpro
공식 코드Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
Papers with Code 아카이브
Prompt Engineering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 16
- 보존된 평가 기록
- 191
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/eabdullin/optimouse-quest
공식 코드https://github.com/ecofri/cxr_llava
공식 코드CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images
https://github.com/egozverev/shold-it-be-executed-or-processed
공식 코드Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?
https://github.com/ejunprung/llm-agents
공식 코드Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineering
https://github.com/ekkkkki/llm-replace-annotators-in-biomedical-text-mining
공식 코드https://github.com/eladlev/autoprompt
공식 코드Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases
https://github.com/elaine-sui/tps
공식 코드Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models
https://github.com/ellenzzn/persllm
공식 코드PersLLM: A Personified Training Approach for Large Language Models
https://github.com/elvishelvis/lca-on-the-line
공식 코드LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies
https://github.com/epfl-dlab/aiflows
공식 코드https://github.com/epfl-dlab/cc_flows
공식 코드https://github.com/event-ahu/safe_largevlm
공식 코드Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
https://github.com/ezzzli/prompt-pate
공식 코드Exploring the Benefits of Visual Prompting in Differential Privacy
https://github.com/facebookresearch/segment-anything
공식 코드https://github.com/felifri/magbig
공식 코드https://github.com/feyzaakyurek/bbnli
공식 코드On Measuring Social Biases in Prompt-Based Multi-Task Learning
https://github.com/funz-0/promptad
공식 코드PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
https://github.com/gair-nlp/cognition-engineering
공식 코드Generative AI Act II: Test Time Scaling Drives Cognition Engineering
https://github.com/gair-nlp/deepresearcher
공식 코드DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
https://github.com/galirage/gpr-bench
공식 코드https://github.com/gaopengcuhk/clip-adapter
공식 코드CLIP-Adapter: Better Vision-Language Models with Feature Adapters
https://github.com/gavin001201/cof
공식 코드CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
https://github.com/geode-project/ner-llm
공식 코드Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models
https://github.com/georgevern/lmcor
공식 코드Small Language Models Improve Giants by Rewriting Their Outputs
https://github.com/ghazaleh-mahmoodi/Prompting_LLMs_AS_Explainable_Metrics
공식 코드Exploring Prompting Large Language Models as Explainable Metrics
https://github.com/giorgosfatouros/Financial-Sentiment-Analysis-with-ChatGPT
공식 코드Transforming Sentiment Analysis in the Financial Domain with ChatGPT
https://github.com/giubots/vision-enabled-dialogue
공식 코드I Was Blind but Now I See: Implementing Vision-Enabled Dialogue in Social Robots
https://github.com/google-research-datasets/uicrit
공식 코드UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset
https://github.com/guoyangzhao/tsclip
공식 코드TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition
https://github.com/hadidaneshmand/ot-transformer
공식 코드Provable optimal transport with transformers: The essence of depth and prompt engineering
https://github.com/hahahawu/long-to-short-via-model-merging
공식 코드Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
https://github.com/hamedtaherkhani/epic
공식 코드EPiC: Cost-effective Search-based Prompt Engineering of LLMs for Code Generation
https://github.com/harderthenharder/transformers_tasks
공식 코드Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models
https://github.com/harrylaucngd/prompt-eng-master
공식 코드Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
https://github.com/hasan-rakibul/llmpathy
공식 코드Labels Generated by Large Language Model Helps Measuring People's Empathy in Vitro
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.