SOTAVerified

Prompt Engineering

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.

Papers

Showing 10011050 of 1236 papers

TitleStatusHype
DeepLocalization: Using change point detection for Temporal Action Localization0
Defining and Evaluating Physical Safety for Large Language Models0
Demonstration Notebook: Finding the Most Suited In-Context Learning Example from Interactions0
Atoxia: Red-teaming Large Language Models with Target Toxic Answers0
Detecting Natural Language Biases with Prompt-based Learning0
Detection and Positive Reconstruction of Cognitive Distortion sentences: Mandarin Dataset and Evaluation0
Détection d’anomalies textuelles à base de l’ingénierie d’invite (Prompt Engineering-Based Text Anomaly Detection )0
Determinants of LLM-assisted Decision-Making0
DevBots can co-design APIs0
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs0
DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation0
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models0
Disentangling Exploration of Large Language Models by Optimal Exploitation0
Diverse Prompts: Illuminating the Prompt Space of Large Language Models with MAP-Elites0
DMRL: Data- and Model-aware Reward Learning for Data Extraction0
Document-Level Event Extraction with Definition-Driven ICL0
Does Prompt Design Impact Quality of Data Imputation by LLMs?0
Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain0
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack0
Do Prompt Patterns Affect Code Quality? A First Empirical Assessment of ChatGPT-Generated Code0
DreamWalk: Style Space Exploration using Diffusion Guidance0
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines0
Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction0
Dual-Modal Prototype Joint Learning for Compositional Zero-Shot Learning0
DUPE: Detection Undermining via Prompt Engineering for Deepfake Text0
Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning0
DynamicMind: A Tri-Mode Thinking System for Large Language Models0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models0
EDGE: Efficient Data Selection for LLM Agents via Guideline Effectiveness0
Editing Knowledge Representation of Language Model via Rephrased Prefix Prompts0
Educational Personalized Learning Path Planning with Large Language Models0
EEG-Defender: Defending against Jailbreak through Early Exit Generation of Large Language Models0
Effects of a Prompt Engineering Intervention on Undergraduate Students' AI Self-Efficacy, AI Knowledge and Prompt Engineering Ability: A Mixed Methods Study0
Effects of Prompt Length on Domain-specific Tasks for Large Language Models0
Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection0
Efficient In-Domain Question Answering for Resource-Constrained Environments0
Efficient Prompting for LLM-based Generative Internet of Things0
Efficient Prompting Methods for Large Language Models: A Survey0
Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy0
Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models0
Emotional Support with LLM-based Empathetic Dialogue Generation0
Empowering ChatGPT-Like Large-Scale Language Models with Local Knowledge Base for Industrial Prognostics and Health Management0
Enabling On-Device LLMs Personalization with Smartphone Sensing0
Enhancing Agricultural Machinery Management through Advanced LLM Integration0
Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models0
Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation0
Enhancing Generalization in Chain of Thought Reasoning for Smaller Models0
Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning0
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies0
Enhancing Security of AI-Based Code Synthesis with GitHub Copilot via Cheap and Efficient Prompt-Engineering0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean77.62Unverified
2Customized EnsembleHarmonic mean75.49Unverified
3MMRLHarmonic mean74.45Unverified
4MMRL++Harmonic mean74.44Unverified
5CoPromptHarmonic mean74.33Unverified
6HPT++Harmonic mean74.24Unverified
7HPTHarmonic mean74.17Unverified
8ProMetaRHarmonic mean74.09Unverified
9MetaPromptHarmonic mean74.02Unverified
10DePTHarmonic mean74.02Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean97.77Unverified
2HPT++Harmonic mean96.96Unverified
3MMRL++Harmonic mean96.75Unverified
4MMRLHarmonic mean96.68Unverified
5HPTHarmonic mean96.65Unverified
6CoPromptHarmonic mean96.55Unverified
7MetaPromptHarmonic mean96.32Unverified
8DePTHarmonic mean96.28Unverified
9ProMetaRHarmonic mean96.16Unverified
10RPOHarmonic mean96.03Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean77.94Unverified
2MMRL++Harmonic mean74.46Unverified
3HPT++Harmonic mean74.23Unverified
4MMRLHarmonic mean73.82Unverified
5CoPromptHarmonic mean72.79Unverified
6ProMetaRHarmonic mean72.31Unverified
7HPTHarmonic mean72.16Unverified
8PromptSRCHarmonic mean71.75Unverified
9DePTHarmonic mean71.09Unverified
10RPOHarmonic mean68.61Unverified
#ModelMetricClaimedVerifiedStatus
1MMRL++Harmonic mean91.94Unverified
2PromptKDHarmonic mean89.14Unverified
3HPT++Harmonic mean87.36Unverified
4MMRLHarmonic mean87.21Unverified
5CoPromptHarmonic mean85.84Unverified
6ProMetaRHarmonic mean85.3Unverified
7DePTHarmonic mean84.88Unverified
8HPTHarmonic mean84.82Unverified
9MetaPromptHarmonic mean83.38Unverified
10MaPLeHarmonic mean82.35Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean45.17Unverified
2MMRL++Harmonic mean42.24Unverified
3HPT++Harmonic mean41.33Unverified
4MMRLHarmonic mean41.15Unverified
5DePTHarmonic mean40.73Unverified
6HPTHarmonic mean40.28Unverified
7ProMetaRHarmonic mean40.25Unverified
8PromptSRCHarmonic mean40.15Unverified
9CoPromptHarmonic mean39.76Unverified
10MetaPromptHarmonic mean38.24Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean90.24Unverified
2HPTHarmonic mean87.16Unverified
3MMRL++Harmonic mean87.01Unverified
4MMRLHarmonic mean86.78Unverified
5ProMetaRHarmonic mean86.7Unverified
6DePTHarmonic mean86.46Unverified
7PromptSRCHarmonic mean85.95Unverified
8HPT++Harmonic mean85.85Unverified
9CoPromptHarmonic mean85.71Unverified
10MetaPromptHarmonic mean84.52Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean97.15Unverified
2HPT++Harmonic mean96.91Unverified
3CoPromptHarmonic mean96.87Unverified
4MMRLHarmonic mean96.74Unverified
5HPTHarmonic mean96.71Unverified
6MaPLeHarmonic mean96.58Unverified
7MMRL++Harmonic mean96.51Unverified
8ProMetaRHarmonic mean96.49Unverified
9CoCoOpHarmonic mean96.43Unverified
10DePTHarmonic mean96.37Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean83.13Unverified
2MMRL++Harmonic mean78.18Unverified
3MMRLHarmonic mean78.06Unverified
4DePTHarmonic mean77.79Unverified
5ProMetaRHarmonic mean76.72Unverified
6PromptSRCHarmonic mean76.58Unverified
7CoPromptHarmonic mean75.66Unverified
8HPT++Harmonic mean75.59Unverified
9HPTHarmonic mean75.57Unverified
10MetaPromptHarmonic mean75.48Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean82.6Unverified
2CoPromptHarmonic mean81.31Unverified
3MMRL++Harmonic mean81.28Unverified
4MMRLHarmonic mean81.2Unverified
5HPT++Harmonic mean81.11Unverified
6DePTHarmonic mean81.06Unverified
7HPTHarmonic mean80.88Unverified
8ProMetaRHarmonic mean80.82Unverified
9MetaPromptHarmonic mean80.62Unverified
10PromptSRCHarmonic mean80.52Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean86.1Unverified
2MMRLHarmonic mean83.89Unverified
3HPT++Harmonic mean83.81Unverified
4MMRL++Harmonic mean83.81Unverified
5ProMetaRHarmonic mean83.25Unverified
6HPTHarmonic mean83.16Unverified
7CoPromptHarmonic mean83.07Unverified
8PromptSRCHarmonic mean82.74Unverified
9DePTHarmonic mean82.46Unverified
10MetaPromptHarmonic mean81.35Unverified
#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean93.05Unverified
2CoPromptHarmonic mean91.4Unverified
3MaPLeHarmonic mean91.38Unverified
4ProMetaRHarmonic mean91.34Unverified
5MetaPromptHarmonic mean91.29Unverified
6DePTHarmonic mean91.22Unverified
7MMRL++Harmonic mean91.1Unverified
8PromptSRCHarmonic mean91.1Unverified
9HPT++Harmonic mean91.09Unverified
10MMRLHarmonic mean91.03Unverified
#ModelMetricClaimedVerifiedStatus
1POMPTop-1 accuracy %51.6Unverified
2MMRLTop-1 accuracy %51.2Unverified
3HPT++Top-1 accuracy %51.18Unverified
4MaPLeTop-1 accuracy %50.9Unverified
5PromptSRCTop-1 accuracy %50.9Unverified
6HPTTop-1 accuracy %50.85Unverified
7CoCoOpTop-1 accuracy %50.63Unverified
8CoPromptTop-1 accuracy %50.5Unverified
9CLIPTop-1 accuracy %47.77Unverified
#ModelMetricClaimedVerifiedStatus
1POMPTop-1 accuracy %77.9Unverified
2PromptSRCTop-1 accuracy %77.8Unverified
3MMRLTop-1 accuracy %77.53Unverified
4HPT++Top-1 accuracy %77.52Unverified
5CoPromptTop-1 accuracy %77.51Unverified
6HPTTop-1 accuracy %77.38Unverified
7MaPLeTop-1 accuracy %76.98Unverified
8CoCoOPTop-1 accuracy %76.18Unverified
9CLIPTop-1 accuracy %73.96Unverified
#ModelMetricClaimedVerifiedStatus
1POMPTop-1 accuracy %49.8Unverified
2PromptSRCTop-1 accuracy %49.55Unverified
3CoPromptTop-1 accuracy %49.43Unverified
4HPTTop-1 accuracy %49.36Unverified
5HPT++Top-1 accuracy %49.28Unverified
6MMRLTop-1 accuracy %49.17Unverified
7MaPLeTop-1 accuracy %49.15Unverified
8CoCoOpTop-1 accuracy %48.75Unverified
9CLIPTop-1 accuracy %46.15Unverified
#ModelMetricClaimedVerifiedStatus
1HPT++Top-1 accuracy %65.31Unverified
2HPTTop-1 accuracy %65.25Unverified
3MMRLTop-1 accuracy %64.47Unverified
4PromptSRCTop-1 accuracy %64.35Unverified
5CoCoOpTop-1 accuracy %64.07Unverified
6MaPLeTop-1 accuracy %64.07Unverified
7POMPTop-1 accuracy %63.8Unverified
8CLIPTop-1 accuracy %60.83Unverified
#ModelMetricClaimedVerifiedStatus
1POMPAccuracy25.3Unverified
2VPTAccuracy24.8Unverified