Predict the potential research impact (0–1) from title & abstract.

Scientific Methodology

• Training Data: Model trained on an extensive dataset of published papers in CS.CV, CS.CL, CS.AI
• Optimization: NDCG optimization with Sigmoid activation and MSE loss
• Validation: Cross-validated against historical citation data
• Architecture: Advanced transformer-based (LLaMA derivative) textual encoder
• Metrics: Quantitative analysis of citation patterns and research influence

Rating Scale

Example Papers

Attention Is All You Need
Score: 0.982 | Grade: AAA 🌟
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Revolutionary paper that introduced the Transformer architecture.

Language Models are Few-Shot Learners
Score: 0.956 | Grade: AAA 🌟
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions—something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.
Groundbreaking GPT-3 paper on few-shot learning.

An Empirical Study of Neural Network Training Protocols
Score: 0.623 | Grade: BBB 🔵
This paper presents a comparative analysis of different training protocols for neural networks across various architectures. We examine the effects of learning rate schedules, batch size selection, and optimization algorithms on model convergence and final performance. Our experiments span multiple datasets and model sizes, providing practical insights for deep learning practitioners.
Solid empirical comparison of training protocols.