ChatAnything: Facetime Chat with LLM-Enhanced Personas
Paper
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2311.06772
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Published
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34
LMM&MML
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Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 28 -
A Survey on Language Models for Code
Paper • 2311.07989 • Published • 21 -
Instruction-Following Evaluation for Large Language Models
Paper • 2311.07911 • Published • 19 -
Prompt Engineering a Prompt Engineer
Paper • 2311.05661 • Published • 20 -
Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities
Paper • 2311.05698 • Published • 9 -
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Paper • 2311.05997 • Published • 36 -
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
Paper • 2311.06242 • Published • 85 -
The ART of LLM Refinement: Ask, Refine, and Trust
Paper • 2311.07961 • Published • 10 -
Pre-training Small Base LMs with Fewer Tokens
Paper • 2404.08634 • Published • 34 -
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Paper • 2404.08197 • Published • 27 -
Multi-Head Mixture-of-Experts
Paper • 2404.15045 • Published • 59 -
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126 -
Pegasus-v1 Technical Report
Paper • 2404.14687 • Published • 30 -
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Paper • 2403.09611 • Published • 124 -
TokenPacker: Efficient Visual Projector for Multimodal LLM
Paper • 2407.02392 • Published • 21 -
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Paper • 2407.03320 • Published • 92 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 119