Raffel, Colin

A Survey on Data Selection for Language Models

Albalak, A., Elazar, Y., Xie, S. M., Longpre, S., Lambert, N., Wang, X., Muennighoff, N., Hou, B., Pan, L., Jeong, H., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12

Building Machine Learning Models Like Open Source Software

Raffel, C. (2023) · Communications of the ACM

Open Source Software InnovationsSoftware Engineering Techniques and Practices

Cited by 10

What Language Model to Train if You Have One Million GPU Hours?

Scao, T. L., Wang, T., Hesslow, D., Saulnier, L., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Muennighoff, N., Phang, J., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Petals: Collaborative Inference and Fine-tuning of Large Models

Borzunov, A., Baranchuk, D., Dettmers, T., Ryabinin, M., Belkada, Y., Chumachenko, A., Samygin, P., & Raffel, C. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model

Deng, H., & Raffel, C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 5

FineWeb2: One Pipeline to Scale Them All - Adapting Pre-Training Data Processing to Every Language

Penedo, G., Kydlíček, H., Sabolčec, V., Messmer, B., Foroutan, N., Kargaran, A. H., Raffel, C., Jaggi, M., Von Werra, L., & Wolf, T. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0