Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
Paper
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2403.09029
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Published
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54
cool-papers
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LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 24 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 67 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 75 -
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Paper • 2403.08763 • Published • 49 -
Language models scale reliably with over-training and on downstream tasks
Paper • 2403.08540 • Published • 14 -
Algorithmic progress in language models
Paper • 2403.05812 • Published • 18 -
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Paper • 2403.05530 • Published • 61 -
TnT-LLM: Text Mining at Scale with Large Language Models
Paper • 2403.12173 • Published • 19 -
Larimar: Large Language Models with Episodic Memory Control
Paper • 2403.11901 • Published • 32 -
Reverse Training to Nurse the Reversal Curse
Paper • 2403.13799 • Published • 13 -
When Do We Not Need Larger Vision Models?
Paper • 2403.13043 • Published • 25 -
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Paper • 2403.14624 • Published • 51 -
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
Paper • 2403.15246 • Published • 9 -
Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
The Unreasonable Ineffectiveness of the Deeper Layers
Paper • 2403.17887 • Published • 78 -
Long-form factuality in large language models
Paper • 2403.18802 • Published • 24 -
BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
Paper • 2403.18421 • Published • 22 -
Octopus v2: On-device language model for super agent
Paper • 2404.01744 • Published • 57 -
Poro 34B and the Blessing of Multilinguality
Paper • 2404.01856 • Published • 13 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 36 -
Training LLMs over Neurally Compressed Text
Paper • 2404.03626 • Published • 21 -
CodeEditorBench: Evaluating Code Editing Capability of Large Language Models
Paper • 2404.03543 • Published • 15 -
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Paper • 2404.02575 • Published • 48 -
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Paper • 2404.12253 • Published • 54 -
Compression Represents Intelligence Linearly
Paper • 2404.09937 • Published • 27 -
Flamingo: a Visual Language Model for Few-Shot Learning
Paper • 2204.14198 • Published • 14 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 41 -
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 29 -
To Believe or Not to Believe Your LLM
Paper • 2406.02543 • Published • 32 -
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
Paper • 2406.06469 • Published • 24 -
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
Paper • 2406.19280 • Published • 62 -
Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems
Paper • 2407.01370 • Published • 86 -
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages
Paper • 2407.03321 • Published • 15 -
Training Task Experts through Retrieval Based Distillation
Paper • 2407.05463 • Published • 8 -
InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct
Paper • 2407.05700 • Published • 11 -
AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?
Paper • 2407.15711 • Published • 9 -
MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
Paper • 2407.20183 • Published • 41 -
OmniParser for Pure Vision Based GUI Agent
Paper • 2408.00203 • Published • 24 -
Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models
Paper • 2408.06663 • Published • 16 -
To Code, or Not To Code? Exploring Impact of Code in Pre-training
Paper • 2408.10914 • Published • 41 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 124 -
MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
Paper • 2408.13257 • Published • 26 -
General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
Paper • 2409.01704 • Published • 83 -
ContextCite: Attributing Model Generation to Context
Paper • 2409.00729 • Published • 14 -
Attention Heads of Large Language Models: A Survey
Paper • 2409.03752 • Published • 89 -
A Controlled Study on Long Context Extension and Generalization in LLMs
Paper • 2409.12181 • Published • 44 -
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Paper • 2409.12183 • Published • 37 -
Attention Prompting on Image for Large Vision-Language Models
Paper • 2409.17143 • Published • 7 -
Can Models Learn Skill Composition from Examples?
Paper • 2409.19808 • Published • 9 -
Law of the Weakest Link: Cross Capabilities of Large Language Models
Paper • 2409.19951 • Published • 54 -
LLaVA-Critic: Learning to Evaluate Multimodal Models
Paper • 2410.02712 • Published • 35 -
From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Paper • 2411.03590 • Published • 10 -
Autoregressive Models in Vision: A Survey
Paper • 2411.05902 • Published • 17 -
Stronger Models are NOT Stronger Teachers for Instruction Tuning
Paper • 2411.07133 • Published • 35