Rho-1: Not All Tokens Are What You Need
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2404.07965
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Published
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84
LM Training
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VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
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Instruction-tuned Language Models are Better Knowledge Learners
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DoRA: Weight-Decomposed Low-Rank Adaptation
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Reverse Training to Nurse the Reversal Curse
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SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
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ReFT: Representation Finetuning for Language Models
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Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
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Learn Your Reference Model for Real Good Alignment
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Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
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Pre-training Small Base LMs with Fewer Tokens
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JetMoE: Reaching Llama2 Performance with 0.1M Dollars
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MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
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SambaLingo: Teaching Large Language Models New Languages
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Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 44 -
Poro 34B and the Blessing of Multilinguality
Paper • 2404.01856 • Published • 13 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 254 -
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
Paper • 2404.13208 • Published • 38 -
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Paper • 2404.05892 • Published • 31 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 138 -
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
Paper • 2404.14619 • Published • 126 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 104 -
Make Your LLM Fully Utilize the Context
Paper • 2404.16811 • Published • 52 -
Tele-FLM Technical Report
Paper • 2404.16645 • Published • 17 -
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
Paper • 2404.16994 • Published • 35 -
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report
Paper • 2405.00732 • Published • 118 -
Iterative Reasoning Preference Optimization
Paper • 2404.19733 • Published • 47 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 100 -
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Paper • 2405.12130 • Published • 46 -
Your Transformer is Secretly Linear
Paper • 2405.12250 • Published • 150 -
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Paper • 2405.20541 • Published • 21 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 30 -
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Paper • 2406.08464 • Published • 65 -
The Llama 3 Herd of Models
Paper • 2407.21783 • Published • 107 -
Gemma 2: Improving Open Language Models at a Practical Size
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MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Paper • 2407.21770 • Published • 22 -
LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
Paper • 2408.07055 • Published • 65 -
Data curation via joint example selection further accelerates multimodal learning
Paper • 2406.17711 • Published • 3 -
Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
Paper • 2408.12570 • Published • 30 -
OLMoE: Open Mixture-of-Experts Language Models
Paper • 2409.02060 • Published • 77 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 135 -
GRIN: GRadient-INformed MoE
Paper • 2409.12136 • Published • 15 -
Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey
Paper • 2409.11564 • Published • 19 -
NVLM: Open Frontier-Class Multimodal LLMs
Paper • 2409.11402 • Published • 72 -
Instruction Following without Instruction Tuning
Paper • 2409.14254 • Published • 27 -
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
Paper • 2409.17146 • Published • 103 -
Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale
Paper • 2409.17115 • Published • 59 -
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
Paper • 2406.14546 • Published • 2 -
Thinking LLMs: General Instruction Following with Thought Generation
Paper • 2410.10630 • Published • 17