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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 22 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 82 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 145 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2410.10934
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SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
Paper • 2409.05556 • Published • 2 -
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
Paper • 2409.04109 • Published • 43 -
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Paper • 2409.15277 • Published • 35 -
Learning Task Decomposition to Assist Humans in Competitive Programming
Paper • 2406.04604 • Published • 4
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What You Say = What You Want? Teaching Humans to Articulate Requirements for LLMs
Paper • 2409.08775 • Published -
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering
Paper • 2409.08250 • Published • 1 -
Synthetic continued pretraining
Paper • 2409.07431 • Published • 2 -
WonderWorld: Interactive 3D Scene Generation from a Single Image
Paper • 2406.09394 • Published • 3
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The Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines
Paper • 2408.01050 • Published • 8 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 54 -
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Paper • 2409.02795 • Published • 72 -
Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
Paper • 2409.04593 • Published • 24
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LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 13 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 17 -
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Paper • 2407.21770 • Published • 22 -
Agent-as-a-Judge: Evaluate Agents with Agents
Paper • 2410.10934 • Published • 18
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Attention Is All You Need
Paper • 1706.03762 • Published • 50 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 13 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 17 -
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Paper • 2407.21770 • Published • 22
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Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 44 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 36 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 60
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AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Paper • 2402.15506 • Published • 14 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 24 -
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Paper • 2405.19893 • Published • 31 -
Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Paper • 2405.19888 • Published • 7
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 145 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 12 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 53 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 45