https://github.com/MohamedBayan/Arabic-Legal-Judgment-Prediction
공식 코드Can Large Language Models Predict the Outcome of Judicial Decisions?
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Can Large Language Models Predict the Outcome of Judicial Decisions?
SG-Bench: Evaluating LLM Safety Generalization Across Diverse Tasks and Prompt Types
LLMs4Life: Large Language Models for Ontology Learning in Life Sciences
V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?
GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models
Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought
Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models
AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Don't Command, Cultivate: An Exploratory Study of System-2 Alignment
Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering
Adaptive In-conversation Team Building for Language Model Agents
Ignore Previous Prompt: Attack Techniques For Language Models
Linear Cross-document Event Coreference Resolution with X-AMR
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
A Survey on Mathematical Reasoning and Optimization with Large Language Models
Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.