Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection | Paper Citation Record

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This paper cites write newline.

Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection write newline

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Semdedup: Data-efficient learning at web-scale through semantic deduplication

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Diverse client selection for federated learning via submodular maximization

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Vicreg: Variance-invariance-covariance regularization for self-supervised learning

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Pythia: A suite for analyzing large language models across training and scaling

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Piqa: Reasoning about physical commonsense in natural language

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Language Models are Few-Shot Learners

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection A simple framework for contrastive learning of visual representations

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Palm: Scaling language modeling with pathways

Reference 10

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Palm: Scaling language modeling with pathways

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Redpajama: an open dataset for training large language models, 2023

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Flashattention: Fast and memory-efficient exact attention with io-awareness

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping

Reference 17

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Submodular functions and optimization

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection A framework for few-shot language model evaluation, 07 2024

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This paper cites Openllama: An open reproduction of llama, May 2023.

Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Openllama: An open reproduction of llama, May 2023

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Online submodular set cover, ranking, and repeated active learning

Reference 21

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Measuring massive multitask language understanding

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Training Compute-Optimal Large Language Models

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Diversified batch selection for training acceleration

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Unsupervised Dense Information Retrieval with Contrastive Learning

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Efficient data subset selection to generalize training across models: transductive and inductive networks

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Fine-tuning with reserved majority for noise reduction

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Understanding dimensional collapse in contrastive self-supervised learning

Reference 28

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Orient: Submodular mutual information measures for data subset selection under distribution shift

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Learning from less data: A unified data subset selection and active learning framework for computer vision

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Prism: A rich class of parameterized submodular information measures for guided data subset selection

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Submodular function maximization

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection An end-to-end submodular framework for data-efficient in-context learning

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Disentangling hate in online memes

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection StarCoder: may the source be with you!

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Unresolved cited work

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Optimal selection of limited vocabulary speech corpora

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Decoupled weight decay regularization

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Can a suit of armor conduct electricity? a new dataset for open book question answering

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection An analysis of approximations for maximizing submodular set functions—i

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Pytorch: An imperative style, high-performance deep learning library

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Carbon Emissions and Large Neural Network Training

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Language models are unsupervised multitask learners

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Learning transferable visual models from natural language supervision

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Scaling Language Models: Methods, Analysis & Insights from Training Gopher

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Ingenious: Using informative data subsets for efficient pre-training of language models

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Winogrande: An adversarial winograd schema challenge at scale

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Discrete location theory

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Discrete location theory

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Weakly Submodular Function Maximization Using Local Submodularity Ratio

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Mimicking the oracle: An initial phase decorrelation approach for class incremental learning

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Beyond neural scaling laws: beating power law scaling via data pruning

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection LaMDA: Language Models for Dialog Applications

Reference 57

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection D4: Improving llm pretraining via document de-duplication and diversification

Reference 58

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection LLaMA: Open and Efficient Foundation Language Models

Reference 60

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Visualizing data using t-sne

Reference 61

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Reconstruct the Pruned Model without Any Retraining

Reference 62

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Submodularity in data subset selection and active learning

Reference 63

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Qurating: Selecting high-quality data for training language models

Reference 64

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Doremi: Optimizing data mixtures speeds up language model pretraining

Reference 65

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Data selection for language models via importance resampling

Reference 66

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

Reference 67

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection On the vulnerability of safety alignment in open-access llms

Reference 68

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Barlow twins: Self-supervised learning via redundancy reduction

Reference 69

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 70

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection TinyLlama: An Open-Source Small Language Model

Reference 71

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Communication-efficient decentralized online continuous dr-submodular maximization

Reference 72

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Boosting Gradient Ascent for Continuous DR-submodular Maximization

Reference 73

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency

Reference 74

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection OPT: Open Pre-trained Transformer Language Models

Reference 75

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Minimax curriculum learning: Machine teaching with desirable difficulties and scheduled diversity

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:30:51.918959Z

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection @esa (Ref

Reference 77

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Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Unresolved cited work

Reference 78

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Reference 79

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