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 451–500 of 1236 papers

TitleStatusHype
Prompt Engineering a Schizophrenia Chatbot: Utilizing a Multi-Agent Approach for Enhanced Compliance with Prompt Instructions—0
A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts—0
Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations—0
Exploring Prompt Engineering: A Systematic Review with SWOT Analysis—0
Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray FluoroscopyCode0
Auto-Evolve: Enhancing Large Language Model's Performance via Self-Reasoning Framework—0
Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video GenerationCode2
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture—0
Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends—0
Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models—0
Enriching Ontologies with Disjointness Axioms using Large Language ModelsCode0
Coal Mining Question Answering with LLMs—0
Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning EnvironmentsCode0
Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models—0
Cognitive Biases in Large Language Models for News Recommendation—0
CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text GenerationCode1
Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT—0
Automatic deductive coding in discourse analysis: an application of large language models in learning analyticsCode0
FanCric : Multi-Agentic Framework for Crafting Fantasy 11 Cricket Teams—0
RGD: Multi-LLM Based Agent Debugger via Refinement and Generation GuidanceCode0
A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particlesCode1
GEMS: Generative Expert Metric System through Iterative Prompt Priming—0
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with FeedbackCode0
A Looming Replication Crisis in Evaluating Behavior in Language Models? Evidence and Solutions—0
Thematic Analysis with Open-Source Generative AI and Machine Learning: A New Method for Inductive Qualitative Codebook Development—0
Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical TeamsCode0
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree SearchCode1
Judgment of Thoughts: Courtroom of the Binary Logical Reasoning in Large Language Models—0
Counterfactual Token Generation in Large Language ModelsCode1
GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design—0
Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses—0
Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection—0
TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign RecognitionCode0
Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error CorrectionCode0
Learning from Contrastive Prompts: Automated Optimization and Adaptation—0
Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs—0
Privacy Policy Analysis through Prompt Engineering for LLMs—0
Automotive innovation landscaping using LLM—0
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option ShufflingCode0
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI ExpertsCode2
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification—0
Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts—0
Using Large Language Models to Generate Clinical Trial Tables and Figures—0
A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network—0
Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness—0
A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks—0
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Benchmark Results

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