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 151200 of 1236 papers

TitleStatusHype
Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token PredictionCode1
Large (Vision) Language Models are Unsupervised In-Context LearnersCode1
CoTAL: Human-in-the-Loop Prompt Engineering, Chain-of-Thought Reasoning, and Active Learning for Generalizable Formative Assessment Scoring0
LearNAT: Learning NL2SQL with AST-guided Task Decomposition for Large Language Models0
GeoRAG: A Question-Answering Approach from a Geographical Perspective0
Text Speaks Louder than Vision: ASCII Art Reveals Textual Biases in Vision-Language Models0
Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG0
GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement0
Task Tokens: A Flexible Approach to Adapting Behavior Foundation Models0
Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models0
Modeling Challenging Patient Interactions: LLMs for Medical Communication Training0
Cognitive Prompts Using Guilford's Structure of Intellect Model0
HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery0
Unlocking the Potential of Past Research: Using Generative AI to Reconstruct Healthcare Simulation Models0
A Measure Based Generalizable Approach to Understandability0
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction0
Unlocking Efficient Long-to-Short LLM Reasoning with Model MergingCode2
A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts0
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 Translation0
Optimizing Photonic Structures with Large Language Model Driven Algorithm DiscoveryCode0
Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation0
MMCR: Advancing Visual Language Model in Multimodal Multi-Turn Contextual Reasoning0
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM0
Strategic Prompt Pricing for AIGC Services: A User-Centric Approach0
A Survey on Mathematical Reasoning and Optimization with Large Language ModelsCode0
When Debate Fails: Bias Reinforcement in Large Language Models0
Enhancing Zero-Shot Image Recognition in Vision-Language Models through Human-like Concept Guidance0
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems0
Synthetic Data Generation Using Large Language Models: Advances in Text and Code0
Organ-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering0
3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o0
Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs0
A Survey on the Optimization of Large Language Model-based AgentsCode3
Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce0
Prompt Sentiment: The Catalyst for LLM Change0
MoLEx: Mixture of Layer Experts for Finetuning with Sparse UpcyclingCode0
The Power of One: A Single Example is All it Takes for Segmentation in VLMs0
"Well, Keep Thinking": Enhancing LLM Reasoning with Adaptive Injection Decoding0
Phishsense-1B: A Technical Perspective on an AI-Powered Phishing Detection Model0
Rethinking Prompt-based Debiasing in Large Language Models0
Evaluating the Generalizability of LLMs in Automated Program Repair0
Lend a Hand: Semi Training-Free Cued Speech Recognition via MLLM-Driven Hand Modeling for Barrier-free CommunicationCode0
Modeling Variants of Prompts for Vision-Language ModelsCode0
Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation0
Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Models0
MMRL: Multi-Modal Representation Learning for Vision-Language ModelsCode2
Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies0
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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
7PromptSRCHarmonic mean91.1Unverified
8MMRL++Harmonic 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
4PromptSRCTop-1 accuracy %50.9Unverified
5MaPLeTop-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