Search Results for author: Gabriel Ilharco

Found 13 papers, 5 papers with code

Robust fine-tuning of zero-shot models

1 code implementation4 Sep 2021 Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

Compared to standard fine-tuning, the resulting weight-space ensembles provide large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy.

Fine-tuning

Finetuning Pretrained Transformers into RNNs

1 code implementation EMNLP 2021 Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith

Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.

Language Modelling Machine Translation +1

Evaluating NLP Models via Contrast Sets

no code implementations1 Oct 2020 Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, A. Zhang, Ben Zhou

Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.

Reading Comprehension Sentiment Analysis

Probing Contextual Language Models for Common Ground with Visual Representations

no code implementations NAACL 2021 Gabriel Ilharco, Rowan Zellers, Ali Farhadi, Hannaneh Hajishirzi

The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations.

Representation Learning

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

3 code implementations15 Feb 2020 Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, Noah Smith

We publicly release all of our experimental data, including training and validation scores for 2, 100 trials, to encourage further analysis of training dynamics during fine-tuning.

Fine-tuning

General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping

1 code implementation11 Jul 2019 Gabriel Ilharco, Vihan Jain, Alexander Ku, Eugene Ie, Jason Baldridge

We address fundamental flaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents.

Dynamic Time Warping Time Series

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