---
title: "Colin Raffel — all works — Elamur Research"
description: "All 107 works of Colin Raffel in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/colin-raffel/works/"
---
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# Colin Raffel — all works

107 works · page 1 of 2 · 1–100 · [← Colin Raffel](https://md.research.elamur.ai/free/en/authors/colin-raffel/)

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1. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
2. **[mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Linting Xue, Noah Constant, Adam Roberts, et al.*2021 · 3,226 citations*
3. **[FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence ↗](https://arxiv.org/abs/2001.07685) arXiv (Cornell University) · no summary yet**Kihyuk Sohn et al.*2020 · 2,292 citations*
4. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,269 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
5. [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,612 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
6. **[BLOOM: A 176B-Parameter Open-Access Multilingual Language Model ↗](https://arxiv.org/abs/2211.05100) arXiv:2211.05100 · no summary yet**BigScience Workshop et al.*2022 · 1,606 citations*
7. **[How Much Knowledge Can You Pack Into the Parameters of a Language Model? ↗](https://aclanthology.org/2020.emnlp-main.437/) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yet**Adam Roberts, Colin Raffel, Noam Shazeer*2020 · 1,100 citations*
8. [**Extracting Training Data from Large Language Models arXiv:2012.07805**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 955 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
9. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 754 citations*
10. **[ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models ↗](https://aclanthology.org/2022.tacl-1.17/) Transactions of the Association for Computational Linguistics · no summary yet**Linting Xue et al.*2022 · 676 citations*
11. **[MixMatch: A Holistic Approach to Semi-Supervised Learning ↗](https://arxiv.org/abs/1905.02249) arXiv (Cornell University) · no summary yet**David Berthelot et al.*2019 · 666 citations*
12. **[The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 434 citations*
13. **[Thermometer Encoding: One Hot Way To Resist Adversarial Examples ↗](https://openalex.org/W2786118190) International Conference on Learning Representations · no summary yet**Jacob Buckman et al.*2018 · 433 citations*
14. [**Large Language Models Struggle to Learn Long-Tail Knowledge arXiv:2211.08411**Nikhil Kandpal, Haikang Deng, Adam Roberts, et al.*2022 · 366 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/)
15. **[TIES-Merging: Resolving Interference When Merging Models](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Prateek Yadav, Derek Tam, Leshem Choshen, et al.*2023 · 324 citations*
16. **[A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music ↗](https://arxiv.org/abs/1803.05428) arXiv (Cornell University) · no summary yet**Roberts, Adam et al.*2018 · 296 citations*
17. **[Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems ↗](https://arxiv.org/abs/1512.08756) arXiv (Cornell University) · no summary yet**Colin Raffel, Daniel P. W. Ellis*2015 · 286 citations*
18. **[Mir_Eval: A Transparent Implementation Of Common Mir Metrics. ↗](https://doi.org/10.5281/zenodo.1416527) no summary yet**Colin Raffel et al.*2014 · 282 citations*
19. **[Online and Linear-Time Attention by Enforcing Monotonic Alignments ↗](https://arxiv.org/abs/1704.00784) arXiv (Cornell University) · no summary yet**Colin Raffel et al.*2017 · 267 citations*
20. **[Monotonic Infinite Lookback Attention for Simultaneous Machine Translation](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-monotonic-infinite-lookback-attention-simultaneous/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, et al.*2019 · 263 citations*
21. **[An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 254 citations*
22. **[Scaling Data-Constrained Language Models ↗](https://arxiv.org/abs/2305.16264) arXiv:2305.16264 · no summary yet**Niklas Muennighoff et al.*2023 · 249 citations*
23. **[Realistic Evaluation of Deep Semi-Supervised Learning Algorithms ↗](https://arxiv.org/abs/1804.09170) arXiv (Cornell University) · no summary yet**Avital Oliver et al.*2018 · 241 citations*
24. **[Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition ↗](https://arxiv.org/abs/1903.10346) arXiv (Cornell University) · no summary yet**Yao Qin et al.*2019 · 227 citations*
25. **[Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Matena, Colin Raffel*2021 · 208 citations*
26. **[WT5?! Training Text-to-Text Models to Explain their Predictions ↗](https://arxiv.org/abs/2004.14546) arXiv:2004.14546 · no summary yet**Sharan Narang et al.*2020 · 199 citations*
27. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP arXiv:2112.10508**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 191 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/)
28. **[Improving and Simplifying Pattern Exploiting Training](https://md.research.elamur.ai/free/en/papers/tam-2021-improving-simplifying-pattern-exploiting-training/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Derek Tam, Rakesh R. Menon, Mohit Bansal, et al.*2021 · 189 citations*
29. [**PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts arXiv:2202.01279**Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, et al.*2022 · 174 citations*](https://md.research.elamur.ai/free/en/papers/bach-2022-promptsource-integrated-development-environment-repository/)
30. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models arXiv:2202.06539**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/)
31. **[ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring ↗](https://arxiv.org/abs/1911.09785) arXiv (Cornell University) · no summary yet**David Berthelot et al.*2020 · 171 citations*
32. **[Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching ↗](https://doi.org/10.7916/d8n58mhv) no summary yet**Colin Raffel*2016 · 154 citations*
33. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
34. **[Scaling Up Models and Data with t5x and seqio ↗](https://arxiv.org/abs/2203.17189) arXiv:2203.17189 · no summary yet**Adam Roberts et al.*2022 · 130 citations*
35. **[Do Transformer Modifications Transfer Across Implementations and Applications? ↗](https://aclanthology.org/2021.emnlp-main.465/) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yet**Sharan Narang et al.*2021 · 123 citations*
36. **[Evaluating the Factual Consistency of Large Language Models Through News Summarization ↗](https://aclanthology.org/2023.findings-acl.322/) Findings of the Association for Computational Linguistics: ACL 2023 · no summary yet**Derek Tam et al.*2023 · 118 citations*
37. **[NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned ↗](https://arxiv.org/abs/2101.00133) arXiv (Cornell University) · no summary yet**Sewon Min et al.*2021 · 108 citations*
38. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language arXiv:2506.20920**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 99 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/)
39. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
40. **[Optimizing DTW-based audio-to-MIDI alignment and matching ↗](https://doi.org/10.1109/icassp.2016.7471641) IEEE International Conference on Acoustics Speech and Signal Processing · no summary yet**Colin Raffel, Daniel P. W. Ellis*2016 · 92 citations*
41. **[What Language Model to Train if You Have One Million GPU Hours? ↗](https://arxiv.org/abs/2210.15424) arXiv:2210.15424 · no summary yet**Teven Le Scao et al.*2022 · 73 citations*
42. **[Training Neural Networks with Fixed Sparse Masks ↗](https://arxiv.org/abs/2111.09839) arXiv (Cornell University) · no summary yet**Yi-Lin Sung, Varun Nair, Colin Raffel*2021 · 72 citations*
43. **[What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? ↗](https://arxiv.org/abs/2204.05832) arXiv:2204.05832 · no summary yet**Thomas Wang et al.*2022 · 62 citations*
44. **[Learning-based Memory Allocation for C++ Server Workloads ↗](https://doi.org/10.1145/3373376.3378525) International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · no summary yet**Martin Maas et al.*2020 · 60 citations*
45. **[Monotonic Chunkwise Attention ↗](https://arxiv.org/abs/1712.05382) arXiv (Cornell University) · no summary yet**Chung‐Cheng Chiu, Colin Raffel*2017 · 60 citations*
46. **[Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer ↗](https://arxiv.org/abs/1807.07543) arXiv (Cornell University) · no summary yet**David Berthelot et al.*2018 · 59 citations*
47. **[Realistic Evaluation of Semi-Supervised Learning Algorithms. ↗](https://openalex.org/W3136198447) International Conference on Learning Representations · no summary yet**Avital Oliver et al.*2018 · 56 citations*
48. **[Distributed Inference and Fine-tuning of Large Language Models Over The Internet ↗](https://doi.org/10.52202/075280-0540) NeurIPS 2023 · no summary yet**Alexander Borzunov et al.*2023 · 52 citations*
49. **[Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions ↗](https://arxiv.org/abs/1907.02957) arXiv (Cornell University) · no summary yet**Yao Qin et al.*2019 · 52 citations*
50. **[Is Generator Conditioning Causally Related to GAN Performance? ↗](https://arxiv.org/abs/1802.08768) arXiv (Cornell University) · no summary yet**Augustus Odena et al.*2018 · 48 citations*
51. **[Petals: Collaborative Inference and Fine-tuning of Large Models ↗](https://arxiv.org/abs/2209.01188) ACL 2023 (demo) · no summary yet**Alexander Borzunov et al.*2022 · 47 citations*
52. **[Onsets and Frames: Dual-Objective Piano Transcription ↗](https://arxiv.org/abs/1710.11153) arXiv (Cornell University) · no summary yet**Curtis Hawthorne et al.*2017 · 47 citations*
53. **[DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows ↗](https://aclanthology.org/2024.acl-long.208/) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Ajay Patel, Colin Raffel, Chris Callison-Burch*2024 · 43 citations*
54. **[Bidirectional Language Models Are Also Few-shot Learners ↗](https://arxiv.org/abs/2209.14500) arXiv (Cornell University) · no summary yet**Ajay Patel et al.*2022 · 42 citations*
55. **[Towards GAN Benchmarks Which Require Generalization ↗](https://arxiv.org/abs/2001.03653) arXiv (Cornell University) · no summary yet**Ishaan Gulrajani, Colin Raffel, Luke Metz*2020 · 42 citations*
56. **[Knowledge is a Region in Weight Space for Fine-tuned Language Models ↗](https://aclanthology.org/2023.findings-emnlp.95/) Findings of the Association for Computational Linguistics: EMNLP 2023 · no summary yet**Almog Gueta et al.*2023 · 40 citations*
57. **[ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning ↗](https://aclanthology.org/2023.acl-long.46/) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Shachar Don-Yehiya et al.*2023 · 37 citations*
58. [**Efficient Online Data Mixing For Language Model Pre-Training arXiv:2312.02406**Alon Albalak, Liangming Pan, Colin Raffel, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2023-efficient-online-data-mixing-language/)
59. **[Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model ↗](https://aclanthology.org/2023.emnlp-main.721/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Haikang Deng, Colin Raffel*2023 · 34 citations*
60. **[On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition ↗](https://doi.org/10.1145/3447548.3467198) arXiv:2106.03062 · no summary yet**Ching-Yuan Bai et al.*2021 · 30 citations*
61. **[Learning a Latent Space of Multitrack Measures ↗](https://arxiv.org/abs/1806.00195) arXiv (Cornell University) · no summary yet**Ian Simon et al.*2018 · 29 citations*
62. **[Learning Hard Alignments with Variational Inference ↗](https://doi.org/10.1109/icassp.2018.8461977) IEEE International Conference on Acoustics Speech and Signal Processing · no summary yet**Dieterich Lawson et al.*2018 · 26 citations*
63. **[Soft Merging of Experts with Adaptive Routing ↗](https://arxiv.org/abs/2306.03745) arXiv:2306.03745 · no summary yet**Mohammed Muqeeth, Haokun Liu, Colin Raffel*2023 · 24 citations*
64. **[Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks ↗](https://doi.org/10.1609/aaai.v30i1.10013) Proceedings of the AAAI Conference on Artificial Intelligence · no summary yet**Nikolai Yakovenko et al.*2016 · 22 citations*
65. **[The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text ↗](https://doi.org/10.52202/085713-1918) neural information processing systems · no summary yet**Nikhil Kandpal et al.*2025 · 21 citations*
66. **[Learning to Route Among Specialized Experts for Zero-Shot Generalization ↗](https://arxiv.org/abs/2402.05859) arXiv (Cornell University) · no summary yet**Muqeeth, Mohammed et al.*2024 · 21 citations*
67. **[Robust and Generalizable Visual Representation Learning via Random Convolutions ↗](https://arxiv.org/abs/2007.13003) arXiv (Cornell University) · no summary yet**Xu, Zhenlin et al.*2020 · 21 citations*
68. **[Merging by Matching Models in Task Parameter Subspaces ↗](https://arxiv.org/abs/2312.04339) arXiv (Cornell University) · no summary yet**Derek Tam, Mohit Bansal, Colin Raffel*2023 · 20 citations*
69. **[Large-Scale Content-Based Matching Of Midi And Audio Files. ↗](https://doi.org/10.5281/zenodo.1417370) Zenodo (CERN European Organization for Nuclear Research) · no summary yet**Colin Raffel, Daniel P. W. Ellis*2015 · 20 citations*
70. **[Extracting Ground-Truth Information From Midi Files: A Midifesto. ↗](https://doi.org/10.5281/zenodo.1418232) Zenodo (CERN European Organization for Nuclear Research) · no summary yet**Colin Raffel, Daniel P. W. Ellis*2016 · 17 citations*
71. **[A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning ↗](https://arxiv.org/abs/2408.07057) arXiv (Cornell University) · no summary yet**Prateek Yadav et al.*2024 · 16 citations*
72. **[Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models ↗](https://arxiv.org/abs/2404.05567) arXiv:2404.05567 · no summary yet**Bowen Pan et al.*2024 · 14 citations*
73. **[Deflecting Adversarial Attacks ↗](https://arxiv.org/abs/2002.07405) arXiv (Cornell University) · no summary yet**Yao Qin et al.*2020 · 13 citations*
74. **[Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples ↗](https://arxiv.org/abs/2002.06224) arXiv (Cornell University) · no summary yet**Samarth Sinha et al.*2020 · 13 citations*
75. **[ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization ↗](https://arxiv.org/abs/2311.13171) arXiv:2311.13171 · no summary yet**Prateek Yadav et al.*2023 · 11 citations*
76. **[Hickle: A HDF5-based python pickle replacement ↗](https://doi.org/10.21105/joss.01115) The Journal of Open Source Software · no summary yet**Danny C. Price et al.*2018 · 11 citations*
77. **[Learning with Limited Text Data ↗](https://aclanthology.org/2022.acl-tutorials.5/) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts · no summary yet**Diyi Yang, Ankur Parikh, Colin Raffel*2022 · 10 citations*
78. **[Pruning subsequence search with attention-based embedding ↗](https://doi.org/10.1109/icassp.2016.7471736) IEEE International Conference on Acoustics Speech and Signal Processing · no summary yet**Colin Raffel, Daniel P. W. Ellis*2016 · 10 citations*
79. **[Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data ↗](https://arxiv.org/abs/2302.00674) arXiv:2302.00674 · no summary yet**Alon Albalak, Colin Raffel, William Yang Wang*2023 · 9 citations*
80. **[Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models ↗](https://arxiv.org/abs/2306.04529) arXiv (Cornell University) · no summary yet**Nikhil Kandpal et al.*2023 · 9 citations*
81. **[Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language ↗](https://doi.org/10.52202/068431-1818) neural information processing systems · no summary yet**Zhenlin Xu, Marc Niethammer, Colin Raffel*2022 · 8 citations*
82. **[Explaining the Learning Dynamics of Direct Feedback Alignment ↗](https://openalex.org/W2619620204) International Conference on Learning Representations · no summary yet**Justin Gilmer et al.*2017 · 7 citations*
83. **[Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games ↗](https://arxiv.org/abs/1509.06731) arXiv (Cornell University) · no summary yet**Nikolai Yakovenko et al.*2015 · 5 citations*
84. **[Position: The Most Expensive Part of an LLM should be its Training Data ↗](https://arxiv.org/abs/2504.12427) arXiv (Cornell University) · no summary yet**Nikhil Kandpal, Colin Raffel*2025 · 4 citations*
85. **[Realistic Evaluation of Model Merging for Compositional Generalization ↗](https://arxiv.org/abs/2409.18314) arXiv (Cornell University) · no summary yet**Derek Tam et al.*2024 · 4 citations*
86. **[TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior ↗](https://arxiv.org/abs/2512.20757) arXiv (Cornell University) · no summary yet**Gül Sena Altıntaş et al.*2025 · 3 citations*
87. **[The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions ↗](https://arxiv.org/abs/2506.13234) arXiv (Cornell University) · no summary yet**Devin Kwok et al.*2025 · 2 citations*
88. **[Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator ↗](https://arxiv.org/abs/2507.18807) arXiv (Cornell University) · no summary yet**Li, YuXin et al.*2025 · 2 citations*
89. **[AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution ↗](https://arxiv.org/abs/2411.15102) arXiv (Cornell University) · no summary yet**Fengyuan Liu, Nikhil Kandpal, Colin Raffel*2024 · 2 citations*
90. **[Estimating timing and channel distortion across related signals ↗](https://doi.org/10.1109/icassp.2014.6853677) IEEE International Conference on Acoustics Speech and Signal Processing · no summary yet**Colin Raffel, Daniel P. W. Ellis*2014 · 2 citations*
91. **[How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data ↗](https://arxiv.org/abs/2604.13977) arXiv (Cornell University) · no summary yet**Joel Niklaus et al.*2026 · 1 citation*
92. **[Efficiently Estimating Data Efficiency for Language Model Fine-tuning ↗](https://arxiv.org/abs/2512.24991) arXiv (Cornell University) · no summary yet**Gyung Hyun Je, Colin Raffel*2025 · 1 citation*
93. **[A Large-scale Study on Training Sample Memorization in Generative Modeling ↗](https://openalex.org/W3131420621) no summary yet**Ching-Yuan Bai et al.*2021 · 1 citation*
94. **[Improving Interpolation in Autoencoders ↗](https://openalex.org/W2948559147) no summary yet**David Berthelot et al.*2019 · 1 citation*
95. **[Evaluating Implicit Generative Models With Large Samples ↗](https://openalex.org/W2908066675) no summary yet**Ishaan Gulrajani, Colin Raffel, Luke Metz*2018 · 1 citation*
96. **[Training a Subsampling Mechanism in Expectation ↗](https://arxiv.org/abs/1702.06914) arXiv (Cornell University) · no summary yet**Colin Raffel, Dieterich Lawson*2017 · 1 citation*
97. **[Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models ↗](https://arxiv.org/abs/2606.11409) arXiv (Cornell University) · no summary yet**Malikeh Ehghaghi et al.*2026*
98. **[Model Merging via Data-Free Covariance Estimation ↗](https://arxiv.org/abs/2604.01329) arXiv (Cornell University) · no summary yet**Marawan Gamal Abdel Hameed et al.*2026*
99. **[The Appeal and Reality of Recycling LoRAs with Adaptive Merging ↗](https://arxiv.org/abs/2602.12323) arXiv (Cornell University) · no summary yet**Haokun Liu et al.*2026*
100. **[FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale ↗](https://arxiv.org/abs/2601.22146) arXiv (Cornell University) · no summary yet**Ajay Patel, Colin Raffel, Chris Callison-Burch*2026*

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