LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs
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naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B:Thanks for sharing !
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📊 Papers Impact: Instant AI Grading for Your Research Papers! 🚀
🌟 Introduction
Hello, AI research community! 🎉
Introducing Papers Impact - the revolutionary AI tool that automatically grades and predicts the potential impact of research papers! 🧠💡
VIDraft/PapersImpact
✨ Key Feature: Instant Paper Grading
The core functionality is brilliantly simple: Just enter an arXiv paper ID or URL, and our AI instantly analyzes and grades the paper's potential academic impact! No need to read through the entire paper yourself - our system automatically evaluates the title and abstract to generate a normalized impact score between 0 and 1.
🎯 How It Works
Enter Paper ID or URL: Simply paste an arXiv ID (e.g., "2504.11651") or full URL
Automatic Fetching: The system retrieves the paper's title and abstract
AI Analysis: Our advanced LLaMA-based transformer model analyzes the content
Instant Grading: Receive an impact score and corresponding letter grade in seconds!
💡 Who Can Benefit?
🔬 Researchers: Pre-assess your paper before submission
📚 Students: Quickly gauge the quality of papers for literature reviews
🏫 Educators: Objectively evaluate student research
📊 Research Managers: Prioritize which papers to read in depth
🧩 Journal Editors: Get an AI second opinion on submissions
🚀 Technical Details
Our model is trained on an extensive dataset of published papers in CS.CV, CS.CL, and CS.AI fields, using NDCG optimization with Sigmoid activation and MSE loss. It's been rigorously cross-validated against historical citation data to ensure accurate impact predictions.
🌟 Introduction
Hello, AI research community! 🎉
Introducing Papers Impact - the revolutionary AI tool that automatically grades and predicts the potential impact of research papers! 🧠💡
VIDraft/PapersImpact
✨ Key Feature: Instant Paper Grading
The core functionality is brilliantly simple: Just enter an arXiv paper ID or URL, and our AI instantly analyzes and grades the paper's potential academic impact! No need to read through the entire paper yourself - our system automatically evaluates the title and abstract to generate a normalized impact score between 0 and 1.
🎯 How It Works
Enter Paper ID or URL: Simply paste an arXiv ID (e.g., "2504.11651") or full URL
Automatic Fetching: The system retrieves the paper's title and abstract
AI Analysis: Our advanced LLaMA-based transformer model analyzes the content
Instant Grading: Receive an impact score and corresponding letter grade in seconds!
💡 Who Can Benefit?
🔬 Researchers: Pre-assess your paper before submission
📚 Students: Quickly gauge the quality of papers for literature reviews
🏫 Educators: Objectively evaluate student research
📊 Research Managers: Prioritize which papers to read in depth
🧩 Journal Editors: Get an AI second opinion on submissions
🚀 Technical Details
Our model is trained on an extensive dataset of published papers in CS.CV, CS.CL, and CS.AI fields, using NDCG optimization with Sigmoid activation and MSE loss. It's been rigorously cross-validated against historical citation data to ensure accurate impact predictions.
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Exciting breakthrough in e-commerce recommendation systems!
Walmart Global Tech researchers have developed a novel Triple Modality Fusion (TMF) framework that revolutionizes how we make product recommendations.
>> Key Innovation
The framework ingeniously combines three distinct data types:
- Visual data to capture product aesthetics and context
- Textual information for detailed product features
- Graph data to understand complex user-item relationships
>> Technical Architecture
The system leverages a Large Language Model (Llama2-7B) as its backbone and introduces several sophisticated components:
Modality Fusion Module
- All-Modality Self-Attention (AMSA) for unified representation
- Cross-Modality Attention (CMA) mechanism for deep feature integration
- Custom FFN adapters to align different modality embeddings
Advanced Training Strategy
- Curriculum learning approach with three complexity levels
- Parameter-Efficient Fine-Tuning using LoRA
- Special token system for behavior and item representation
>> Real-World Impact
The results are remarkable:
- 38.25% improvement in Electronics recommendations
- 43.09% boost in Sports category accuracy
- Significantly higher human evaluation scores compared to traditional methods
Currently deployed in Walmart's production environment, this research demonstrates how combining multiple data modalities with advanced LLM architectures can dramatically improve recommendation accuracy and user satisfaction.
Walmart Global Tech researchers have developed a novel Triple Modality Fusion (TMF) framework that revolutionizes how we make product recommendations.
>> Key Innovation
The framework ingeniously combines three distinct data types:
- Visual data to capture product aesthetics and context
- Textual information for detailed product features
- Graph data to understand complex user-item relationships
>> Technical Architecture
The system leverages a Large Language Model (Llama2-7B) as its backbone and introduces several sophisticated components:
Modality Fusion Module
- All-Modality Self-Attention (AMSA) for unified representation
- Cross-Modality Attention (CMA) mechanism for deep feature integration
- Custom FFN adapters to align different modality embeddings
Advanced Training Strategy
- Curriculum learning approach with three complexity levels
- Parameter-Efficient Fine-Tuning using LoRA
- Special token system for behavior and item representation
>> Real-World Impact
The results are remarkable:
- 38.25% improvement in Electronics recommendations
- 43.09% boost in Sports category accuracy
- Significantly higher human evaluation scores compared to traditional methods
Currently deployed in Walmart's production environment, this research demonstrates how combining multiple data modalities with advanced LLM architectures can dramatically improve recommendation accuracy and user satisfaction.
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