-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 20 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 75 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 135 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 22
Collections
Discover the best community collections!
Collections including paper arxiv:2402.10200
-
World Model on Million-Length Video And Language With RingAttention
Paper • 2402.08268 • Published • 33 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 75 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 91 -
FiT: Flexible Vision Transformer for Diffusion Model
Paper • 2402.12376 • Published • 48
-
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots
Paper • 2405.07990 • Published • 15 -
Large Language Models as Planning Domain Generators
Paper • 2405.06650 • Published • 8 -
AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
Paper • 2404.12753 • Published • 39 -
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Paper • 2404.07972 • Published • 41
-
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 91 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 104 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 58 -
Do language models plan ahead for future tokens?
Paper • 2404.00859 • Published • 2
-
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 99 -
sDPO: Don't Use Your Data All at Once
Paper • 2403.19270 • Published • 32 -
ViTAR: Vision Transformer with Any Resolution
Paper • 2403.18361 • Published • 48 -
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 42
-
Linearity of Relation Decoding in Transformer Language Models
Paper • 2308.09124 • Published • 2 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 91 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 102
-
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Paper • 1701.06538 • Published • 4 -
Attention Is All You Need
Paper • 1706.03762 • Published • 37 -
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 11 -
Language Model Evaluation Beyond Perplexity
Paper • 2106.00085 • Published
-
Contrastive Decoding Improves Reasoning in Large Language Models
Paper • 2309.09117 • Published • 37 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 91 -
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Paper • 2403.14624 • Published • 50 -
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Paper • 2402.12875 • Published • 2
-
JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention
Paper • 2310.00535 • Published • 2 -
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
Paper • 2211.00593 • Published • 2 -
Rethinking Interpretability in the Era of Large Language Models
Paper • 2402.01761 • Published • 19 -
Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla
Paper • 2307.09458 • Published • 9