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Collections including paper arxiv:2502.14502

Rethinking Data Selection at Scale: Random Selection is Almost All You Need

Paper • 2410.09335 • Published Oct 12, 2024 • 16
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning

Paper • 2410.06456 • Published Oct 9, 2024 • 37
Emergent properties with repeated examples

Paper • 2410.07041 • Published Oct 9, 2024 • 8
Personalized Visual Instruction Tuning

Paper • 2410.07113 • Published Oct 9, 2024 • 70

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Paper • 2502.14802 • Published Feb 20, 2025 • 13
When an LLM is apprehensive about its answers -- and when its uncertainty is justified

Paper • 2503.01688 • Published Mar 3, 2025 • 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, 2025 • 91
s-nlp/Llama-3.1-8B-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24, 2025 • 91k • 23
s-nlp/Llama-3.1-8B-Instruct-DBpedia-HighlyKnown

Viewer • Updated Feb 24, 2025 • 21k • 11
s-nlp/Mistral-7b-0.3-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24, 2025 • 91k • 39

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91
SIFT: Grounding LLM Reasoning in Contexts via Stickers

Paper • 2502.14922 • Published Feb 19, 2025 • 32
SurveyX: Academic Survey Automation via Large Language Models

Paper • 2502.14776 • Published Feb 20, 2025 • 100
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment

Paper • 2502.16894 • Published Feb 24, 2025 • 32

LLM Pruning and Distillation in Practice: The Minitron Approach

Paper • 2408.11796 • Published Aug 21, 2024 • 58
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering

Paper • 2408.09174 • Published Aug 17, 2024 • 52
To Code, or Not To Code? Exploring Impact of Code in Pre-training

Paper • 2408.10914 • Published Aug 20, 2024 • 45
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications

Paper • 2408.11878 • Published Aug 20, 2024 • 63

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

Paper • 2502.15086 • Published Feb 20, 2025 • 16
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20, 2025 • 91
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information

Paper • 2502.14258 • Published Feb 20, 2025 • 26
S^2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning

Paper • 2502.12853 • Published Feb 18, 2025 • 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, 2025 • 194
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Paper • 2502.14739 • Published Feb 20, 2025 • 108
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20, 2025 • 91
PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

Paper • 2502.14282 • Published Feb 20, 2025 • 29

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Paper • 2502.14786 • Published Feb 20, 2025 • 157

Rethinking Data Selection at Scale: Random Selection is Almost All You Need

Paper • 2410.09335 • Published Oct 12, 2024 • 16
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning

Paper • 2410.06456 • Published Oct 9, 2024 • 37
Emergent properties with repeated examples

Paper • 2410.07041 • Published Oct 9, 2024 • 8
Personalized Visual Instruction Tuning

Paper • 2410.07113 • Published Oct 9, 2024 • 70

LLM Pruning and Distillation in Practice: The Minitron Approach

Paper • 2408.11796 • Published Aug 21, 2024 • 58
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering

Paper • 2408.09174 • Published Aug 17, 2024 • 52
To Code, or Not To Code? Exploring Impact of Code in Pre-training

Paper • 2408.10914 • Published Aug 20, 2024 • 45
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications

Paper • 2408.11878 • Published Aug 20, 2024 • 63

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Paper • 2502.14802 • Published Feb 20, 2025 • 13
When an LLM is apprehensive about its answers -- and when its uncertainty is justified

Paper • 2503.01688 • Published Mar 3, 2025 • 21

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

Paper • 2502.15086 • Published Feb 20, 2025 • 16
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20, 2025 • 91
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information

Paper • 2502.14258 • Published Feb 20, 2025 • 26
S^2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning

Paper • 2502.12853 • Published Feb 18, 2025 • 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, 2025 • 91
s-nlp/Llama-3.1-8B-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24, 2025 • 91k • 23
s-nlp/Llama-3.1-8B-Instruct-DBpedia-HighlyKnown

Viewer • Updated Feb 24, 2025 • 21k • 11
s-nlp/Mistral-7b-0.3-Instruct-TriviaQA-HighlyKnown

Viewer • Updated Feb 24, 2025 • 91k • 39

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, 2025 • 194
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Paper • 2502.14739 • Published Feb 20, 2025 • 108
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?

Paper • 2502.14502 • Published Feb 20, 2025 • 91
PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

Paper • 2502.14282 • Published Feb 20, 2025 • 29

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91
SIFT: Grounding LLM Reasoning in Contexts via Stickers

Paper • 2502.14922 • Published Feb 19, 2025 • 32
SurveyX: Academic Survey Automation via Large Language Models

Paper • 2502.14776 • Published Feb 20, 2025 • 100
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment

Paper • 2502.16894 • Published Feb 24, 2025 • 32

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

Paper • 2502.14502 • Published Feb 20, 2025 • 91
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Paper • 2502.14786 • Published Feb 20, 2025 • 157

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