Rethinking Data Selection at Scale: Random Selection is Almost All You Need
Paper
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2410.09335
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Published
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14
LLM Training
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From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning
Paper • 2410.06456 • Published • 35 -
Emergent properties with repeated examples
Paper • 2410.07041 • Published • 8 -
Personalized Visual Instruction Tuning
Paper • 2410.07113 • Published • 69 -
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints
Paper • 2410.06458 • Published • 8 -
What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
Paper • 2410.23743 • Published • 57 -
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
Paper • 2410.24175 • Published • 15 -
GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages
Paper • 2410.23825 • Published • 3 -
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Paper • 2410.21333 • Published • 9 -
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning
Paper • 2410.19290 • Published • 10 -
AutoTrain: No-code training for state-of-the-art models
Paper • 2410.15735 • Published • 56 -
Stronger Models are NOT Stronger Teachers for Instruction Tuning
Paper • 2411.07133 • Published • 15 -
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization
Paper • 2411.06208 • Published • 18 -
LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation
Paper • 2411.04997 • Published • 27 -
Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding
Paper • 2411.04282 • Published • 22 -
Large Language Models Can Self-Improve in Long-context Reasoning
Paper • 2411.08147 • Published • 39