Research Paper Summary Example
See how ClarisMind Synopses transforms academic papers while preserving critical citations and methodology details that generic summarizers miss.
Source Paper
Title: "Selective Attention Improves Transformer"
Authors: Yaniv Leviathan, Matan Kalman, Yossi Matias (Google Research)
Venue: ICLR 2025 (Accepted)
ArXiv: 2410.02703
Original Paper Abstract
"We introduce Selective Attention, a simple parameter-free change to the standard attention mechanism which reduces attention to unneeded elements. Selective Attention consistently improves transformer performance on language modeling and downstream tasks. For language modeling, we show transformers with selective attention can perform equivalently to standard transformers with ~2X more heads and parameters in their attention modules. On downstream tasks, selective attention offers improvements in both memory and computation during inference. For example, transformers trained on C4 with different context sizes can need 16X, 25X, and 47X less memory while maintaining the same validation perplexity. We hypothesize that unneeded elements in the attention's context degrade performance by reducing the model's efficiency."
Generic Summarizer vs ClarisMind Synopses
Generic AI Summarizer Output
"This paper introduces Selective Attention, a new attention mechanism for transformer models. The authors propose a parameter-free modification to standard attention that reduces computational requirements. Their method improves language modeling performance while using less memory and computation during inference. The technique allows transformers to perform as well as larger models with more parameters. They tested their approach on various datasets and found consistent improvements in efficiency and performance. The work addresses limitations in current transformer architectures and provides a practical solution for scaling language models."
Issues with Generic Summary:
- • No citations - loses all reference information
- • Vague methodology - "parameter-free modification" without specifics
- • Missing concrete results - "consistent improvements" but no numbers
- • Generic language - still contains boilerplate like "tested their approach"
- • No technical depth - misses key implementation details
ClarisMind Synopses Output
🔬 Core Innovation
Selective Attention: Parameter-free modification that reduces attention to unneeded elements in transformer context
📊 Key Performance Results
- • Transformers perform equivalently to models with ~2X more heads/parameters
- • Memory reduction: 16X, 25X, and 47X less memory while maintaining same validation perplexity
- • Consistent improvements across language modeling and downstream tasks
⚙️ Technical Implementation
Method reduces attention computation by identifying and filtering unneeded context elements that degrade model efficiency
🏛️ Research Context
Authors: Leviathan, Kalman, Matias (Google Research)
Venue: ICLR 2025 | Reference: ArXiv:2410.02703
💡 Research Impact
Addresses fundamental efficiency bottleneck in transformers by proving "unneeded elements degrade performance"
ClarisMind Synopses' 3 Principles in Action
1. Extract Key Ideas
Identified core innovation (selective attention), specific performance metrics (2X parameter efficiency, 16-47X memory reduction), and technical mechanism
2. Cut Boilerplate
Eliminated academic filler like "tested their approach" and "found consistent improvements" while preserving concrete findings
3. Preserve Evidence
Maintained complete citation (authors, venue, ArXiv ID), specific numerical results, and technical implementation details
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