Models
Datasets
Spaces
Docs
Enterprise
Pricing
Log In
Sign Up

Collections

Discover the best community collections!

Collections including paper arxiv:2502.14502

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 • 54
s-nlp/Llama-3.1-8B-Instruct-DBpedia-HighlyKnown

Viewer • Updated Feb 24 • 21k • 56
s-nlp/Mistral-7b-0.3-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24 • 91k • 50

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 • 100
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment

Paper • 2502.16894 • Published Feb 24 • 32

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

Paper • 2502.14502 • Published Feb 20 • 91

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 • 192
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Paper • 2502.14739 • Published Feb 20 • 104
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 • 29

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 • 155

rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Paper • 2501.04519 • Published Jan 8 • 286
Transformer^2: Self-adaptive LLMs

Paper • 2501.06252 • Published Jan 9 • 54
Multimodal LLMs Can Reason about Aesthetics in Zero-Shot

Paper • 2501.09012 • Published Jan 15 • 10
FAST: Efficient Action Tokenization for Vision-Language-Action Models

Paper • 2501.09747 • Published Jan 16 • 27

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 • 54
s-nlp/Llama-3.1-8B-Instruct-DBpedia-HighlyKnown

Viewer • Updated Feb 24 • 21k • 56
s-nlp/Mistral-7b-0.3-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24 • 91k • 50

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 • 192
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Paper • 2502.14739 • Published Feb 20 • 104
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 • 29

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 • 100
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment

Paper • 2502.16894 • Published Feb 24 • 32

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 • 155

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

Paper • 2502.14502 • Published Feb 20 • 91

rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Paper • 2501.04519 • Published Jan 8 • 286
Transformer^2: Self-adaptive LLMs

Paper • 2501.06252 • Published Jan 9 • 54
Multimodal LLMs Can Reason about Aesthetics in Zero-Shot

Paper • 2501.09012 • Published Jan 15 • 10
FAST: Efficient Action Tokenization for Vision-Language-Action Models

Paper • 2501.09747 • Published Jan 16 • 27

Previous
1
2
Next

Company

TOS Privacy About Jobs

Website

Models Datasets Spaces Pricing Docs