Igor Melnyk

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upvoted a paper 5 months ago

An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels

Paper • 2406.09415 • Published Jun 13 • 50

upvoted 8 papers 6 months ago

ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models

Paper • 2405.09220 • Published May 15 • 24

Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3

Paper • 2405.00664 • Published May 1 • 18

A Careful Examination of Large Language Model Performance on Grade School Arithmetic

Paper • 2405.00332 • Published May 1 • 30

Better & Faster Large Language Models via Multi-token Prediction

Paper • 2404.19737 • Published Apr 30 • 73

Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models

Paper • 2404.18796 • Published Apr 29 • 68

Multi-Head Mixture-of-Experts

Paper • 2404.15045 • Published Apr 23 • 59

SnapKV: LLM Knows What You are Looking for Before Generation

Paper • 2404.14469 • Published Apr 22 • 23

The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions

Paper • 2404.13208 • Published Apr 19 • 38

upvoted 11 papers 7 months ago

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Paper • 2404.12253 • Published Apr 18 • 53

Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models

Paper • 2404.12387 • Published Apr 18 • 38

BLINK: Multimodal Large Language Models Can See but Not Perceive

Paper • 2404.12390 • Published Apr 18 • 24

Learn Your Reference Model for Real Good Alignment

Paper • 2404.09656 • Published Apr 15 • 82

Pre-training Small Base LMs with Fewer Tokens

Paper • 2404.08634 • Published Apr 12 • 34

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84

Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models

Paper • 2404.07973 • Published Apr 11 • 30

RULER: What's the Real Context Size of Your Long-Context Language Models?

Paper • 2404.06654 • Published Apr 9 • 33

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10 • 103

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9 • 64

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27