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 401–450 of 1236 papers

TitleStatusHype
Modeling Challenging Patient Interactions: LLMs for Medical Communication Training—0
Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models—0
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery—0
Cognitive Prompts Using Guilford's Structure of Intellect Model—0
A Measure Based Generalizable Approach to Understandability—0
Unlocking the Potential of Past Research: Using Generative AI to Reconstruct Healthcare Simulation Models—0
Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction—0
A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts—0
LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object IntegrationCode0
HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation—0
Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation—0
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery—0
MMCR: Advancing Visual Language Model in Multimodal Multi-Turn Contextual Reasoning—0
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM—0
Strategic Prompt Pricing for AIGC Services: A User-Centric Approach—0
A Survey on Mathematical Reasoning and Optimization with Large Language ModelsCode0
When Debate Fails: Bias Reinforcement in Large Language Models—0
Enhancing Zero-Shot Image Recognition in Vision-Language Models through Human-like Concept Guidance—0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems—0
Organ-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering—0
Synthetic Data Generation Using Large Language Models: Advances in Text and Code—0
3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o—0
Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs—0
Prompt Sentiment: The Catalyst for LLM Change—0
MoLEx: Mixture of Layer Experts for Finetuning with Sparse UpcyclingCode0
Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce—0
Phishsense-1B: A Technical Perspective on an AI-Powered Phishing Detection Model—0
The Power of One: A Single Example is All it Takes for Segmentation in VLMs—0
"Well, Keep Thinking": Enhancing LLM Reasoning with Adaptive Injection Decoding—0
Rethinking Prompt-based Debiasing in Large Language Models—0
Evaluating the Generalizability of LLMs in Automated Program Repair—0
Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation—0
Lend a Hand: Semi Training-Free Cued Speech Recognition via MLLM-Driven Hand Modeling for Barrier-free CommunicationCode0
Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Models—0
Modeling Variants of Prompts for Vision-Language ModelsCode0
Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies—0
Evaluation of the Automated Labeling Method for Taxonomic Nomenclature Through Prompt-Optimized Large Language Model—0
LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitationsCode0
Generation of Optimized Solidity Code for Machine Learning Models using LLMs—0
Jailbreaking is (Mostly) Simpler Than You Think—0
Cognitive Bias Detection Using Advanced Prompt Engineering—0
ToolFuzz -- Automated Agent Tool Testing—0
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions—0
Can Frontier LLMs Replace Annotators in Biomedical Text Mining? Analyzing Challenges and Exploring SolutionsCode0
Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test—0
Self-Evolved Preference Optimization for Enhancing Mathematical Reasoning in Small Language Models—0
Bandit-Based Prompt Design Strategy Selection Improves Prompt OptimizersCode0
Language-agnostic, automated assessment of listeners' speech recall using large language models—0
NutriGen: Personalized Meal Plan Generator Leveraging Large Language Models to Enhance Dietary and Nutritional AdherenceCode0
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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