Jesse Dodge

Expected Validation Performance and Estimation of a Random Variable's Maximum

2021 · arXiv (Cornell University)

Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation …

Efficient Hierarchical Domain Adaptation for Pretrained Language Models

2021 · arXiv (Cornell University)

The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous sources, but information about the source of the …

Modeling the Machine Learning Multiverse

2022 · arXiv (Cornell University)

Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the multiverse analysis. Our framework builds upon the multiverse analysis (Steegen et …

OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens

2025 · arXiv (Cornell University)

We present OLMoTrace, the first system that traces the outputs of language models back to their full, multi-trillion-token training data in real time. OLMoTrace finds and shows verbatim matches between segments of language model output …

Retrofitting Word Vectors to Semantic Lexicons

2015

Manaal Faruqui, Jesse Dodge, Sujay Kumar Jauhar, Chris Dyer, Eduard Hovy, Noah A. Smith. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

Key-Value Memory Networks for Directly Reading Documents

2016

Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven …

Show Your Work: Improved Reporting of Experimental Results

2019

Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

2020 · arXiv (Cornell University)

Fine-tuning pretrained contextual word embedding models to supervised downstream tasks has become commonplace in natural language processing. This process, however, is often brittle: even with the same hyperparameter values, distinct random seeds can lead to …

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

2022 · arXiv (Cornell University)

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed …