Search Results for author: Tatsunori Hashimoto

Found 18 papers, 10 papers with code

Spurious Correlations in Reference-Free Evaluation of Text Generation

1 code implementation ACL 2022 Esin Durmus, Faisal Ladhak, Tatsunori Hashimoto

Model-based, reference-free evaluation metrics have been proposed as a fast and cost-effective approach to evaluate Natural Language Generation (NLG) systems.

Abstractive Text Summarization Text Generation

Distributionally Robust Models with Parametric Likelihood Ratios

1 code implementation ICLR 2022 Paul Michel, Tatsunori Hashimoto, Graham Neubig

As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yield accurate predictions when confronted with distribution shift.

Text Classification

Language modeling via stochastic processes

1 code implementation ICLR 2022 Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori Hashimoto

TC does this by learning a representation which maps the dynamics of how text changes in a document to the dynamics of a stochastic process of interest.

Language Modelling Text Infilling

Jury Learning: Integrating Dissenting Voices into Machine Learning Models

no code implementations7 Feb 2022 Mitchell L. Gordon, Michelle S. Lam, Joon Sung Park, Kayur Patel, Jeffrey T. Hancock, Tatsunori Hashimoto, Michael S. Bernstein

We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction.

Medical Diagnosis Misinformation

Is Importance Weighting Incompatible with Interpolating Classifiers?

1 code implementation ICLR 2022 Ke Alexander Wang, Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto

As a remedy, we show that polynomially-tailed losses restore the effects of importance reweighting in correcting distribution shift in overparameterized models.

Extending the WILDS Benchmark for Unsupervised Adaptation

no code implementations ICLR 2022 Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.

Large Language Models Can Be Strong Differentially Private Learners

1 code implementation ICLR 2022 Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto

Differentially Private (DP) learning has seen limited success for building large deep learning models of text, and attempts at straightforwardly applying Differentially Private Stochastic Gradient Descent (DP-SGD) to NLP tasks have resulted in large performance drops and high computational overhead.

On the Opportunities and Risks of Foundation Models

no code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates

no code implementations27 Jul 2021 Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, Matei Zaharia

Given a dataset $\mathcal{D}$, we are interested in computing the mean of a subset of $\mathcal{D}$ which matches a predicate.

Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions

1 code implementation ACL 2021 Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori Hashimoto

In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said.

Question Answering

On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies

1 code implementation NAACL 2021 Tianyi Zhang, Tatsunori Hashimoto

We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains.

Language Modelling Masked Language Modeling +1

Modeling the Second Player in Distributionally Robust Optimization

1 code implementation ICLR 2021 Paul Michel, Tatsunori Hashimoto, Graham Neubig

Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set").

Model Selection

Predicting the impact of dataset composition on model performance

no code implementations1 Jan 2021 Tatsunori Hashimoto

Real-world machine learning systems are often are trained using a mix of data sources with varying cost and quality.

Experimental Design Machine Translation +2

Distributionally Robust Losses for Latent Covariate Mixtures

no code implementations28 Jul 2020 John Duchi, Tatsunori Hashimoto, Hongseok Namkoong

While modern large-scale datasets often consist of heterogeneous subpopulations---for example, multiple demographic groups or multiple text corpora---the standard practice of minimizing average loss fails to guarantee uniformly low losses across all subpopulations.

Robustness to Spurious Correlations via Human Annotations

1 code implementation ICML 2020 Megha Srivastava, Tatsunori Hashimoto, Percy Liang

The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions.

Common Sense Reasoning

Improved Natural Language Generation via Loss Truncation

no code implementations ACL 2020 Daniel Kang, Tatsunori Hashimoto

In this work, we show that the distinguishability of the models and reference serves as a principled and robust alternative for handling invalid references.

Text Generation

Inferring Multidimensional Rates of Aging from Cross-Sectional Data

1 code implementation12 Jul 2018 Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nicholas Eriksson, Percy Liang

Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data.

Time Series

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