An Effective Label Noise Model for DNN Text Classification. - Ishan Jindal; Daniel Pressel; Brian Lester; Matthew S. Nokleby (NAACL-HLT (1), 2019)

Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers. - Brian Lester; Daniel Pressel; Amy Hemmeter; Sagnik Ray Choudhury; Srinivas Bangalore (EMNLP (Findings), 2020)

Leader: Prefixing a Length for Faster Word Vector Serialization. - Brian Lester (CoRR, 2020)

Multiple Word Embeddings for Increased Diversity of Representation. - Brian Lester; Daniel Pressel; Amy Hemmeter; Sagnik Ray Choudhury; Srinivas Bangalore (CoRR, 2020)

iobes: A Library for Span-Level Processing. - Brian Lester (CoRR, 2020)

The Power of Scale for Parameter-Efficient Prompt Tuning. - Brian Lester; Rami Al-Rfou; Noah Constant (EMNLP (1), 2021)

Intent Features for Rich Natural Language Understanding. - Brian Lester; Sagnik Ray Choudhury; Rashmi Prasad; Srinivas Bangalore (NAACL-HLT (Industry Papers), 2021)

SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer. - Tu Vu; Brian Lester; Noah Constant; Rami Al-Rfou'; Daniel Cer (ACL (1), 2022)

Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation. - Tu Vu; Aditya Barua; Brian Lester; Daniel Cer; Mohit Iyyer; Noah Constant (EMNLP, 2022)

Finetuned Language Models are Zero-Shot Learners. - Jason Wei; Maarten Bosma; Vincent Y. Zhao; Kelvin Guu; Adams Wei Yu; Brian Lester; Nan Du 002; Andrew M. Dai; Quoc V. Le (ICLR, 2022)

Scaling Up Models and Data with t5x and seqio. - Adam Roberts; Hyung Won Chung; Anselm Levskaya; Gaurav Mishra; James Bradbury; Daniel Andor; Sharan Narang; Brian Lester; Colin Gaffney; Afroz Mohiuddin; Curtis Hawthorne; Aitor Lewkowycz; et al. (CoRR, 2022)

Reducing Retraining by Recycling Parameter-Efficient Prompts. - Brian Lester; Joshua Yurtsever; Siamak Shakeri; Noah Constant (CoRR, 2022)

Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. - Nikhil Kandpal; Brian Lester; Mohammed Muqeeth; Anisha Mascarenhas; Monty Evans; Vishal Baskaran; Tenghao Huang; Haokun Liu; Colin Raffel (ICML, 2023)

Bayesian Optimal Experimental Design for Constitutive Model Calibration. - Denielle Ricciardi; Tom Seidl; Brian Lester; Amanda Jones; Elizabeth Jones (CoRR, 2023)

Scaling Up Models and Data with t5x and seqio. - Adam Roberts; Hyung Won Chung; Gaurav Mishra; Anselm Levskaya; James Bradbury; Daniel Andor; Sharan Narang; Brian Lester; Colin Gaffney; Afroz Mohiuddin; Curtis Hawthorne; Aitor Lewkowycz; et al. (J. Mach. Learn. Res., 2023)

Realistic Evaluation of Model Merging for Compositional Generalization. - Derek Tam; Yash Kant; Brian Lester; Igor Gilitschenski; Colin Raffel (CoRR, 2024)

Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference. - Denielle Ricciardi; Tom Seidl; Brian Lester; Amanda Jones; Elizabeth Jones (CoRR, 2024)

Training LLMs over Neurally Compressed Text. - Brian Lester; Jaehoon Lee 001; Alexander A. Alemi; Jeffrey Pennington; Adam Roberts; Jascha Sohl-Dickstein; Noah Constant (Trans. Mach. Learn. Res., 2024)

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text. - Nikhil Kandpal; Brian Lester; Colin Raffel; Sebastian Majstorovic; Stella Biderman; Baber Abbasi; Luca Soldaini; Enrico Shippole; A. Feder Cooper; Aviya Skowron; John Kirchenbauer; Shayne Longpre; et al. (CoRR, 2025)