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Collections including paper arxiv:2502.14502

about 1 hour ago

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6, 2024 • 29
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6, 2024 • 13
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7, 2024 • 44
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7, 2024 • 23

Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models

Paper • 2502.15086 • Published Feb 20 • 16
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information

Paper • 2502.14258 • Published Feb 20 • 26
S^2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning

Paper • 2502.12853 • Published Feb 18 • 29

收集的感兴趣的AI

MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Paper • 2502.14499 • Published Feb 20 • 193
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Paper • 2502.14739 • Published Feb 20 • 105
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

Paper • 2502.14282 • Published Feb 20 • 20

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Paper • 2502.14786 • Published Feb 20 • 146

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Paper • 2502.14802 • Published Feb 20 • 13
When an LLM is apprehensive about its answers -- and when its uncertainty is justified

Paper • 2503.01688 • Published Mar 3 • 21

Knowledge Packing

Models and datasets from the paper: "How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?" https://arxiv.org/abs/2502.14502

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
s-nlp/Llama-3.1-8B-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24 • 91k • 29
s-nlp/Llama-3.1-8B-Instruct-DBpedia-HighlyKnown

Viewer • Updated Feb 24 • 21k • 24
s-nlp/Mistral-7b-0.3-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24 • 91k • 19

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
SIFT: Grounding LLM Reasoning in Contexts via Stickers

Paper • 2502.14922 • Published Feb 19 • 32
SurveyX: Academic Survey Automation via Large Language Models

Paper • 2502.14776 • Published Feb 20 • 101
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment

Paper • 2502.16894 • Published Feb 24 • 31

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91

about 1 hour ago

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6, 2024 • 29
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6, 2024 • 13
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7, 2024 • 44
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7, 2024 • 23

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Paper • 2502.14802 • Published Feb 20 • 13
When an LLM is apprehensive about its answers -- and when its uncertainty is justified

Paper • 2503.01688 • Published Mar 3 • 21

Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models

Paper • 2502.15086 • Published Feb 20 • 16
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information

Paper • 2502.14258 • Published Feb 20 • 26
S^2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning

Paper • 2502.12853 • Published Feb 18 • 29

Knowledge Packing

Models and datasets from the paper: "How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?" https://arxiv.org/abs/2502.14502

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
s-nlp/Llama-3.1-8B-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24 • 91k • 29
s-nlp/Llama-3.1-8B-Instruct-DBpedia-HighlyKnown

Viewer • Updated Feb 24 • 21k • 24
s-nlp/Mistral-7b-0.3-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24 • 91k • 19

收集的感兴趣的AI

MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Paper • 2502.14499 • Published Feb 20 • 193
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Paper • 2502.14739 • Published Feb 20 • 105
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

Paper • 2502.14282 • Published Feb 20 • 20

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
SIFT: Grounding LLM Reasoning in Contexts via Stickers

Paper • 2502.14922 • Published Feb 19 • 32
SurveyX: Academic Survey Automation via Large Language Models

Paper • 2502.14776 • Published Feb 20 • 101
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment

Paper • 2502.16894 • Published Feb 24 • 31

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Paper • 2502.14786 • Published Feb 20 • 146

How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20 • 91

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