David Heineman

According to our database, David Heineman authored at least 14 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2026

Olmo Hybrid: From Theory to Practice and Back.

William Merrill, Yanhong Li, Tyler Romero, Anej Svete, Caia Costello, Pradeep Dasigi, Dirk Groeneveld, David Heineman, Bailey Kuehl, Nathan Lambert, Chuan Li, Kyle Lo, Saumya Malik, DJ Matusz, Benjamin Minixhofer, Jacob Morrison, Luca Soldaini, Finbarr Timbers, Pete Walsh, Noah A. Smith, Hannaneh Hajishirzi, Ashish Sabharwal

CoRR, April, 2026

Olmix: A Framework for Data Mixing Throughout LM Development.

Mayee F. Chen, Tyler Murray, David Heineman, Matt Jordan, Hannaneh Hajishirzi, Christopher Ré, Luca Soldaini, Kyle Lo

CoRR, February, 2026

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces.

Mike A. Merrill, Alexander Glenn Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Yeon Shin, Thomas Walshe, Estefany Kelly Buchanan, Junhong Shen, Guanghao Ye, Haowei Lin, Jason Poulos, Maoyu Wang, Marianna Nezhurina, Jenia Jitsev, Di Lu, Orfeas Menis-Mastromichalakis, Zhiwei Xu, Zizhao Chen, Yue Liu, Robert Zhang, Leon Liangyu Chen, Anurag Kashyap, Jan-Lucas Uslu, Jeffrey Li, Jianbo Wu, Minghao Yan, Song Bian, Vedang Sharma, Ke Sun, Steven Dillmann, Akshay Anand, Andrew Lanpouthakoun, Bardia Koopah, Changran Hu, Etash Kumar Guha, Gabriel H. S. Dreiman, Jiacheng Zhu, Karl Krauth, Li Zhong, Niklas Muennighoff, Robert Amanfu, Shangyin Tan, Shreyas Pimpalgaonkar, Tushar Aggarwal, Xiangning Lin, Xin Lan, Xuandong Zhao, Yiqing Liang, Yuanli Wang, Zilong Wang, Changzhi Zhou, David Heineman, Hange Liu, Harsh Trivedi, John Yang, Junhong Lin, Manish Shetty, Michael Yang, Nabil Omi, Negin Raoof, Shanda Li, Terry Yue Zhuo, Wuwei Lin, Yiwei Dai, Yuxin Wang, Wenhao Chai, Shang Zhou, Dariush Wahdany, Ziyu She, Jiaming Hu, Zhikang Dong, Yuxuan Zhu, Sasha Cui, Ahson Saiyed, Arinbjörn Kolbeinsson, Jesse Hu, Christopher Michael Rytting, Ryan Marten, Yixin Wang, Alex Dimakis, Andy Konwinski, Ludwig Schmidt

CoRR, January, 2026

2025

Olmo 3.

Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, David Graham, David Heineman, Dirk Groeneveld, Faeze Brahman, Finbarr Timbers, Hamish Ivison, Jacob Morrison, Jake Poznanski, Kyle Lo, Luca Soldaini, Matt Jordan, Mayee F. Chen, Michael Noukhovitch, Nathan Lambert, Pete Walsh, Pradeep Dasigi, Robert Berry, Saumya Malik, Saurabh Shah, Scott Geng, Shane Arora, Shashank Gupta, Taira Anderson, Teng Xiao, Tyler Murray, Tyler Romero, Victoria Graf, Akari Asai, Akshita Bhagia, Alexander Wettig, Alisa Liu, Aman Rangapur, Chloe Anastasiades, Costa Huang, Dustin Schwenk, Harsh Trivedi, Ian Magnusson, Jaron Lochner, Jiacheng Liu, Lester James V. Miranda, Maarten Sap, Malia Morgan, Michael Schmitz, Michal Guerquin, Michael Wilson, Regan Huff, Ronan Le Bras, Rui Xin, Rulin Shao, Sam Skjonsberg, Shannon Zejiang Shen, Shuyue Stella Li, Tucker Wilde, Valentina Pyatkin, William Merrill, Yapei Chang, Yuling Gu, Zhiyuan Zeng, Ashish Sabharwal, Luke Zettlemoyer, Pang Wei Koh, Ali Farhadi, Noah A. Smith, Hannaneh Hajishirzi

CoRR, December, 2025

Fluid Language Model Benchmarking.

Valentin Hofmann, David Heineman, Ian Magnusson, Kyle Lo, Jesse Dodge, Maarten Sap, Pang Wei Koh, Chun Wang, Hannaneh Hajishirzi, Noah A. Smith

CoRR, September, 2025

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation.

David Heineman, Valentin Hofmann, Ian Magnusson, Yuling Gu, Noah A. Smith, Hannaneh Hajishirzi, Kyle Lo, Jesse Dodge

CoRR, August, 2025

Evaluating LLMs on Chinese Idiom Translation.

Cai Yang, Yao Dou, David Heineman, Xiaofeng Wu, Wei Xu

CoRR, August, 2025

DataDecide: How to Predict Best Pretraining Data with Small Experiments.

Ian Magnusson, Nguyen Tai, Ben Bogin, David Heineman, Jena D. Hwang, Luca Soldaini, Akshita Bhagia, Jiacheng Liu, Dirk Groeneveld, Oyvind Tafjord, Noah A. Smith, Pang Wei Koh, Jesse Dodge

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Establishing Task Scaling Laws via Compute-Efficient Model Ladders.

Akshita Bhagia, Jiacheng Liu, Alexander Wettig, David Heineman, Oyvind Tafjord, Ananya Harsh Jha, Luca Soldaini, Noah A. Smith, Dirk Groeneveld, Pang Wei Koh, Jesse Dodge, Hannaneh Hajishirzi

CoRR, 2024

Improving Minimum Bayes Risk Decoding with Multi-Prompt.

David Heineman, Yao Dou, Wei Xu

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Towards a path dependent account of category fluency.

David Heineman, Reba Koenen, Sashank Varma

Proceedings of the 46th Annual Meeting of the Cognitive Science Society, 2024

2023

Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation.

David Heineman, Yao Dou, Wei Xu

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA.

David Heineman, Yao Dou, Mounica Maddela, Wei Xu

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

LENS: A Learnable Evaluation Metric for Text Simplification.

Mounica Maddela, Yao Dou, David Heineman, Wei Xu

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023