Do we really have to filter out random noise in pre-training data for language models? | Paper Citation Record

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This paper cites A pretrainer’s guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity,.

Do we really have to filter out random noise in pre-training data for language models? A pretrainer’s guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity,

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This paper cites What’s in my big data?.

Do we really have to filter out random noise in pre-training data for language models? What’s in my big data?

Reference 2

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Do we really have to filter out random noise in pre-training data for language models? LLaMA: Open and Efficient Foundation Language Models

Reference 3

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This paper cites Physics of language models: Part 3.1, knowledge storage and extraction,.

Do we really have to filter out random noise in pre-training data for language models? Physics of language models: Part 3.1, knowledge storage and extraction,

Reference 4

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This paper cites Data selection for language models via importance resampling,.

Do we really have to filter out random noise in pre-training data for language models? Data selection for language models via importance resampling,

Reference 5

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This paper cites Ai models collapse when trained on recursively generated data,.

Do we really have to filter out random noise in pre-training data for language models? Ai models collapse when trained on recursively generated data,

Reference 6

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This paper cites How bad is training on synthetic data? a statistical analysis of language model collapse,.

Do we really have to filter out random noise in pre-training data for language models? How bad is training on synthetic data? a statistical analysis of language model collapse,

Reference 7

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This paper cites Leveraging Web-Crawled Data for High-Quality Fine-Tuning.

Do we really have to filter out random noise in pre-training data for language models? Leveraging Web-Crawled Data for High-Quality Fine-Tuning

Reference 8

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This paper cites Wavlm: Large-scale self-supervised pre-training for full stack speech processing,.

Do we really have to filter out random noise in pre-training data for language models? Wavlm: Large-scale self-supervised pre-training for full stack speech processing,

Reference 9

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This paper cites Noise-aware learning from web-crawled image-text data for image captioning,.

Do we really have to filter out random noise in pre-training data for language models? Noise-aware learning from web-crawled image-text data for image captioning,

Reference 10

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This paper cites A survey on data selection for language models,.

Do we really have to filter out random noise in pre-training data for language models? A survey on data selection for language models,

Reference 11

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Observation 92057b1e-53da-4d4e-94c9-6cebc1575b13 · outbound

This paper cites Dolma: an open corpus of three trillion tokens for language model pretraining research,.

Do we really have to filter out random noise in pre-training data for language models? Dolma: an open corpus of three trillion tokens for language model pretraining research,

Reference 12

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This paper cites Openwebtext corpus,.

Do we really have to filter out random noise in pre-training data for language models? Openwebtext corpus,

Reference 13

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Observation 7b87a0bf-91bc-4dde-83b0-b4b8762b67b9 · outbound

This paper cites ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution Errors.

Do we really have to filter out random noise in pre-training data for language models? ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution Errors

Reference 14

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Observation eeeb5c56-95d2-4e74-ba1a-1502d5f79c9d · outbound

This paper cites VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model.

Do we really have to filter out random noise in pre-training data for language models? VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model

Reference 15

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This paper cites UniAudio: An Audio Foundation Model Toward Universal Audio Generation.

Do we really have to filter out random noise in pre-training data for language models? UniAudio: An Audio Foundation Model Toward Universal Audio Generation

Reference 16

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This paper cites Understanding and mitigating the label noise in pre-training on downstream tasks,.

Do we really have to filter out random noise in pre-training data for language models? Understanding and mitigating the label noise in pre-training on downstream tasks,

Reference 17

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This paper cites Is out-of-distribution detection learnable?.

Do we really have to filter out random noise in pre-training data for language models? Is out-of-distribution detection learnable?

Reference 18

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This paper cites Defweb: Defending user privacy against cache-based website fingerprinting attacks with intelligent noise injection,.

Do we really have to filter out random noise in pre-training data for language models? Defweb: Defending user privacy against cache-based website fingerprinting attacks with intelligent noise injection,

Reference 19

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This paper cites Language models are unsupervised multitask learners,.

Do we really have to filter out random noise in pre-training data for language models? Language models are unsupervised multitask learners,

Reference 20

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This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Do we really have to filter out random noise in pre-training data for language models? The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 21

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This paper cites Shalev-Shwartz and S.

Do we really have to filter out random noise in pre-training data for language models? Shalev-Shwartz and S

Reference 22

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This paper cites A theory of learning from different domains,.

Do we really have to filter out random noise in pre-training data for language models? A theory of learning from different domains,

Reference 23

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This paper cites A mathematical exploration of why language models help solve downstream tasks,.

Do we really have to filter out random noise in pre-training data for language models? A mathematical exploration of why language models help solve downstream tasks,

Reference 24

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This paper cites Why do pretrained language models help in downstream tasks? an analysis of head and prompt tuning,.

Do we really have to filter out random noise in pre-training data for language models? Why do pretrained language models help in downstream tasks? an analysis of head and prompt tuning,

Reference 25

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Observation b270e9a9-3d55-46d3-88ac-99334c3d53a7 · outbound

This paper cites Same pre-training loss, better downstream: Implicit bias matters for language models,.

Do we really have to filter out random noise in pre-training data for language models? Same pre-training loss, better downstream: Implicit bias matters for language models,

Reference 26

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This paper cites Revisiting discriminative vs. generative classifiers: Theory and implications,.

Do we really have to filter out random noise in pre-training data for language models? Revisiting discriminative vs. generative classifiers: Theory and implications,

Reference 27

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This paper cites How multilingual is multilingual BERT?.

Do we really have to filter out random noise in pre-training data for language models? How multilingual is multilingual BERT?

Reference 28

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This paper cites Finding universal grammatical relations in multilingual BERT,.

Do we really have to filter out random noise in pre-training data for language models? Finding universal grammatical relations in multilingual BERT,

Reference 29

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This paper cites Zeronlg: Aligning and autoencoding domains for zero-shot multimodal and multilingual natural language generation,.

Do we really have to filter out random noise in pre-training data for language models? Zeronlg: Aligning and autoencoding domains for zero-shot multimodal and multilingual natural language generation,

Reference 30

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This paper cites VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning.

Do we really have to filter out random noise in pre-training data for language models? VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning

Reference 31

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This paper cites ALMTokenizer: A Low-bitrate and Semantic-rich Audio Codec Tokenizer for Audio Language Modeling.

Do we really have to filter out random noise in pre-training data for language models? ALMTokenizer: A Low-bitrate and Semantic-rich Audio Codec Tokenizer for Audio Language Modeling

Reference 32

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This paper cites Stochastic collapse: How gradient noise attracts SGD dynamics towards simpler subnetworks,.

Do we really have to filter out random noise in pre-training data for language models? Stochastic collapse: How gradient noise attracts SGD dynamics towards simpler subnetworks,

Reference 33

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This paper cites Positive-negative momentum: Manipulating stochastic gradient noise to improve generalization,.

Do we really have to filter out random noise in pre-training data for language models? Positive-negative momentum: Manipulating stochastic gradient noise to improve generalization,

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This paper cites Noise stability regularization for improving BERT fine-tuning,.

Do we really have to filter out random noise in pre-training data for language models? Noise stability regularization for improving BERT fine-tuning,

Reference 35

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This paper cites A diffusion theory for deep learning dynamics: Stochastic gradient descent exponentially favors flat minima,.

Do we really have to filter out random noise in pre-training data for language models? A diffusion theory for deep learning dynamics: Stochastic gradient descent exponentially favors flat minima,

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This paper cites Unveiling the structure of wide flat minima in neural networks,.

Do we really have to filter out random noise in pre-training data for language models? Unveiling the structure of wide flat minima in neural networks,

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This paper cites The Llama 3 Herd of Models.

Do we really have to filter out random noise in pre-training data for language models? The Llama 3 Herd of Models

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This paper cites Le Gall, Measure theory, probability, and stochastic processes.

Do we really have to filter out random noise in pre-training data for language models? Le Gall, Measure theory, probability, and stochastic processes

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This paper cites Structured pruning of large language models,.

Do we really have to filter out random noise in pre-training data for language models? Structured pruning of large language models,

Reference 40

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This paper cites The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models,.

Do we really have to filter out random noise in pre-training data for language models? The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models,

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This paper cites Plug-and- play: An efficient post-training pruning method for large language models,.

Do we really have to filter out random noise in pre-training data for language models? Plug-and- play: An efficient post-training pruning method for large language models,

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This paper cites LRQuant: Learnable and robust post-training quantization for large language models,.

Do we really have to filter out random noise in pre-training data for language models? LRQuant: Learnable and robust post-training quantization for large language models,

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This paper cites A comprehensive evaluation of quantization strategies for large language models,.

Do we really have to filter out random noise in pre-training data for language models? A comprehensive evaluation of quantization strategies for large language models,

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This paper cites IntactKV: Improving large language model quantization by keeping pivot tokens intact,.

Do we really have to filter out random noise in pre-training data for language models? IntactKV: Improving large language model quantization by keeping pivot tokens intact,

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This paper cites Cost-effective distillation of large language models,.

Do we really have to filter out random noise in pre-training data for language models? Cost-effective distillation of large language models,

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This paper cites Distilling the Knowledge in a Neural Network.

Do we really have to filter out random noise in pre-training data for language models? Distilling the Knowledge in a Neural Network

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This paper cites mGPT: Few-shot learners go multilingual,.

Do we really have to filter out random noise in pre-training data for language models? mGPT: Few-shot learners go multilingual,

Reference 48

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This paper cites Toward understanding generative data augmentation,.

Do we really have to filter out random noise in pre-training data for language models? Toward understanding generative data augmentation,

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Do we really have to filter out random noise in pre-training data for language models? Smoothness, low noise and fast rates,

Reference 50

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This paper cites Diffsound: Discrete diffusion model for text-to-sound generation,.

Do we really have to filter out random noise in pre-training data for language models? Diffsound: Discrete diffusion model for text-to-sound generation,

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This paper cites Towards explainable joint models via information theory for multiple intent detection and slot filling,.

Do we really have to filter out random noise in pre-training data for language models? Towards explainable joint models via information theory for multiple intent detection and slot filling,

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This paper cites Kdpror: A knowledge-decoupling probabilistic framework for video-text retrieval,.

Do we really have to filter out random noise in pre-training data for language models? Kdpror: A knowledge-decoupling probabilistic framework for video-text retrieval,

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This paper cites Gpa: global and prototype alignment for audio-text retrieval,.

Do we really have to filter out random noise in pre-training data for language models? Gpa: global and prototype alignment for audio-text retrieval,

Reference 54

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This paper cites Preparing lessons for progressive training on language models,.

Do we really have to filter out random noise in pre-training data for language models? Preparing lessons for progressive training on language models,

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This paper cites Do as We Do, Not as You Think: the Conformity of Large Language Models.

Do we really have to filter out random noise in pre-training data for language models? Do as We Do, Not as You Think: the Conformity of Large Language Models

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This paper cites Rmt: Retentive networks meet vision transformers,.

Do we really have to filter out random noise in pre-training data for language models? Rmt: Retentive networks meet vision transformers,

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This paper cites Reusing pretrained models by multi-linear operators for efficient training,.

Do we really have to filter out random noise in pre-training data for language models? Reusing pretrained models by multi-linear operators for efficient training,

Reference 58

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This paper cites Semantic Equitable Clustering: A Simple and Effective Strategy for Clustering Vision Tokens.

Do we really have to filter out random noise in pre-training data for language models? Semantic Equitable Clustering: A Simple and Effective Strategy for Clustering Vision Tokens

Reference 59

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This paper cites Pcad: Towards asr- robust spoken language understanding via prototype calibration and asymmetric decoupling,.

Do we really have to filter out random noise in pre-training data for language models? Pcad: Towards asr- robust spoken language understanding via prototype calibration and asymmetric decoupling,

Reference 60

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This paper cites Macsc: Towards multimodal- augmented pre-trained language models via conceptual prototypes and self-balancing calibra- tion,.

Do we really have to filter out random noise in pre-training data for language models? Macsc: Towards multimodal- augmented pre-trained language models via conceptual prototypes and self-balancing calibra- tion,

Reference 61

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This paper cites HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec.

Do we really have to filter out random noise in pre-training data for language models? HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec

Reference 62

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Observation 247aa966-32d4-440b-945a-b96d11c7df17 · outbound

This paper cites UniAudio 1.5: Large Language Model-driven Audio Codec is A Few-shot Audio Task Learner.

Do we really have to filter out random noise in pre-training data for language models? UniAudio 1.5: Large Language Model-driven Audio Codec is A Few-shot Audio Task Learner

Reference 63

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Observation 0fa6f811-b05d-4edc-bc88-d3cdb4d87d0d · outbound

This paper cites FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof.

Do we really have to filter out random noise in pre-training data for language models? FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof

Reference 64

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Observation 24cbec1a-f8e5-4c3d-90fc-2d2cd14ac512 · outbound

This paper cites FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature.

Do we really have to filter out random noise in pre-training data for language models? FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature

Reference 65

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Observation c78fe4ee-1fad-4b44-9d0c-300efe993bd4 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Do we really have to filter out random noise in pre-training data for language models? Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 66

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Observation 53abca1b-6c74-4c3c-ba55-f33eee8ce64b · outbound

This paper cites Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws.

Do we really have to filter out random noise in pre-training data for language models? Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 67

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This paper cites Parameterized algorithms and complexity for the traveling purchaser problem and its variants,.

Do we really have to filter out random noise in pre-training data for language models? Parameterized algorithms and complexity for the traveling purchaser problem and its variants,

Reference 68

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This paper cites Dataset pruning: Reducing training data by examining generalization influence,.

Do we really have to filter out random noise in pre-training data for language models? Dataset pruning: Reducing training data by examining generalization influence,

Reference 69

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This paper cites On training data influence of GPT models,.

Do we really have to filter out random noise in pre-training data for language models? On training data influence of GPT models,

Reference 70

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This paper cites From quantity to quality: Boosting LLM performance with self-guided data selection for instruction tuning,.

Do we really have to filter out random noise in pre-training data for language models? From quantity to quality: Boosting LLM performance with self-guided data selection for instruction tuning,

Reference 71

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Observation f5423c0c-be98-4c67-8dd4-13da3ec33721 · outbound

This paper cites Doremi: Optimizing data mixtures speeds up language model pretraining,.

Do we really have to filter out random noise in pre-training data for language models? Doremi: Optimizing data mixtures speeds up language model pretraining,

Reference 72

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This paper cites Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data.

Do we really have to filter out random noise in pre-training data for language models? Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data

Reference 73

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Observation edb1e5e7-3608-478f-8b80-6b770f8fada6 · outbound

This paper cites Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs.

Do we really have to filter out random noise in pre-training data for language models? Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs

Reference 74

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This paper cites A comprehensive survey on source-free domain adaptation,.

Do we really have to filter out random noise in pre-training data for language models? A comprehensive survey on source-free domain adaptation,

Reference 75

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This paper cites Cross-domain mutual information adversarial maximiza- tion,.

Do we really have to filter out random noise in pre-training data for language models? Cross-domain mutual information adversarial maximiza- tion,

Reference 76

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This paper cites Domain adaptive land-cover classification via local consistency and global diversity,.

Do we really have to filter out random noise in pre-training data for language models? Domain adaptive land-cover classification via local consistency and global diversity,

Reference 77

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This paper cites Diffusion-based probabilistic uncertainty estimation for active domain adaptation,.

Do we really have to filter out random noise in pre-training data for language models? Diffusion-based probabilistic uncertainty estimation for active domain adaptation,

Reference 78

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Observation d46f6f92-ff54-4a9f-a061-090ee4327ae1 · outbound

This paper cites Online adaptive fault diagnosis with test-time domain adaptation,.

Do we really have to filter out random noise in pre-training data for language models? Online adaptive fault diagnosis with test-time domain adaptation,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.832007Z

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Observation 93f0ad21-3f65-4d91-99ec-f10a5728d2fe · outbound

This paper cites Divergence-agnostic unsupervised domain adaptation by adversarial attacks,.

Do we really have to filter out random noise in pre-training data for language models? Divergence-agnostic unsupervised domain adaptation by adversarial attacks,

Reference 80

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Observation 30f9f738-75a4-4e6a-9435-96a8c1fcd894 · outbound

This paper cites Imbalanced open set domain adaptation via moving-threshold estimation and gradual alignment,.

Do we really have to filter out random noise in pre-training data for language models? Imbalanced open set domain adaptation via moving-threshold estimation and gradual alignment,

Reference 81

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Observation a5e4410d-7ab1-421c-b67f-402a748c8713 · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey,.

Do we really have to filter out random noise in pre-training data for language models? Learning from noisy labels with deep neural networks: A survey,

Reference 82

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Observation 80dbfc26-d9e2-40fb-845e-ee3750565c80 · outbound

This paper cites Does label smoothing mitigate label noise?.

Do we really have to filter out random noise in pre-training data for language models? Does label smoothing mitigate label noise?

Reference 83

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Observation ccfdff97-884e-4f24-94b3-bd5932737885 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

Do we really have to filter out random noise in pre-training data for language models? SmoothGrad: removing noise by adding noise

Reference 84

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This paper cites Pure noise to the rescue of insufficient data: Improving imbalanced classification by training on random noise images,.

Do we really have to filter out random noise in pre-training data for language models? Pure noise to the rescue of insufficient data: Improving imbalanced classification by training on random noise images,

Reference 85

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Observation 772f9bf0-235a-43d4-b1a4-3991426f7380 · outbound

This paper cites LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression.

Do we really have to filter out random noise in pre-training data for language models? LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:04:30.053906Z

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Observation f06efcc4-fd35-448f-91a4-da2750cd68f1 · outbound

This paper cites Vision Transformer with Sparse Scan Prior.

Do we really have to filter out random noise in pre-training data for language models? Vision Transformer with Sparse Scan Prior

Reference 87

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This paper cites Semigmmpoint: Semi-supervised point cloud segmentation based on gaussian mixture models,.

Do we really have to filter out random noise in pre-training data for language models? Semigmmpoint: Semi-supervised point cloud segmentation based on gaussian mixture models,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.765395Z

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Observation f86614ab-2c94-482c-bac0-a2e9655febc5 · outbound

This paper cites Composed fine-tuning: Freezing pre-trained denoising autoencoders for improved generalization,.

Do we really have to filter out random noise in pre-training data for language models? Composed fine-tuning: Freezing pre-trained denoising autoencoders for improved generalization,

Reference 89

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Observation 9ecbcaf8-cfc0-46ba-bc3a-b6ec955b262c · outbound

This paper cites Game on tree: Visual hallucination mitigation via coarse-to-fine view tree and game theory,.

Do we really have to filter out random noise in pre-training data for language models? Game on tree: Visual hallucination mitigation via coarse-to-fine view tree and game theory,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.745462Z

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Observation b5edfe61-66e0-4e72-bde9-2e8fed3ccf39 · outbound

This paper cites Not all texts are the same: Dynamically querying texts for scene text detection,.

Do we really have to filter out random noise in pre-training data for language models? Not all texts are the same: Dynamically querying texts for scene text detection,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.734208Z

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Observation da431da8-f5cb-471d-8978-956b43446485 · outbound

This paper cites Towards multimodal-augmented pre-trained language models via self-balanced expectation-maximization iteration,.

Do we really have to filter out random noise in pre-training data for language models? Towards multimodal-augmented pre-trained language models via self-balanced expectation-maximization iteration,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.722785Z

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Observation 92a36130-2c45-45bc-ac40-cf254b3a3596 · outbound

This paper cites VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification.

Do we really have to filter out random noise in pre-training data for language models? VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification

Reference 93

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Observation 615d0e26-bab0-47f2-b697-8d9426c1aadc · outbound

This paper cites Improving pretrained language model fine-tuning with noise stability regularization,.

Do we really have to filter out random noise in pre-training data for language models? Improving pretrained language model fine-tuning with noise stability regularization,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.711292Z

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Observation 2708e4a6-1e28-4ab6-8f69-aa3d9401753d · outbound

This paper cites SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization,.

Do we really have to filter out random noise in pre-training data for language models? SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.699686Z

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Observation 77a5dca0-0548-4d5b-a001-9143f37c2c51 · outbound

This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

Do we really have to filter out random noise in pre-training data for language models? Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 96

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Observation 3c737a18-7d15-4104-b4cd-42b466f07a3a · outbound

This paper cites Learning to prompt for vision-language models,.

Do we really have to filter out random noise in pre-training data for language models? Learning to prompt for vision-language models,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.688043Z

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Observation 9098e3a3-4c5a-4533-ac00-d9aa85b754c1 · outbound

This paper cites Conditional prompt learning for vision-language models,.

Do we really have to filter out random noise in pre-training data for language models? Conditional prompt learning for vision-language models,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:04:30.676767Z

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Observation c7f8ce62-0baf-414d-ad87-05dcaeffe454 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Do we really have to filter out random noise in pre-training data for language models? Learning transferable visual models from natural language supervision,

Reference 99

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This paper cites LoRA: Low-rank adaptation of large language models,.

Do we really have to filter out random noise in pre-training data for language models? LoRA: Low-rank adaptation of large language models,

Reference 100

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