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09/13/2023
Statistical Rejection Sampling Improves Preference Optimization
Improving the alignment of language models with human preferences remain...
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05/17/2023
SLiC-HF: Sequence Likelihood Calibration with Human Feedback
Learning from human feedback has been shown to be effective at aligning ...
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12/20/2022
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
The evaluation of abstractive summarization models typically uses test d...
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09/30/2022
Calibrating Sequence likelihood Improves Conditional Language Generation
Conditional language models are predominantly trained with maximum likel...
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09/30/2022
Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Machine learning algorithms typically assume independent and identically...
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08/08/2022
Investigating Efficiently Extending Transformers for Long Input Summarization
While large pretrained Transformer models have proven highly capable at ...
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08/01/2022
SMART: Sentences as Basic Units for Text Evaluation
Widely used evaluation metrics for text generation either do not work we...
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06/18/2020
SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
Most prior work in the sequence-to-sequence paradigm focused on datasets...
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12/18/2019
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Recent work pre-training Transformers with self-supervised objectives on...
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10/23/2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Transfer learning, where a model is first pre-trained on a data-rich tas...
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10/02/2019
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
We propose an end-to-end neural model for zero-shot abstractive text sum...
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06/07/2019
Likelihood Ratios for Out-of-Distribution Detection
Discriminative neural networks offer little or no performance guarantees...
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05/30/2019
Assessing The Factual Accuracy of Generated Text
We propose a model-based metric to estimate the factual accuracy of gene...
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05/28/2019
Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Massively multi-label prediction/classification problems arise in enviro...
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10/12/2018
Unsupervised Neural Multi-document Abstractive Summarization
Abstractive summarization has been studied using neural sequence transdu...
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08/08/2018
Learning to Write Notes in Electronic Health Records
Clinicians spend a significant amount of time inputting free-form textua...
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01/30/2018
Generating Wikipedia by Summarizing Long Sequences
We show that generating English Wikipedia articles can be approached as ...
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01/24/2018
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipa...
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01/16/2018
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
The driving force behind the recent success of LSTMs has been their abil...
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04/14/2017
Get To The Point: Summarization with Pointer-Generator Networks
Neural sequence-to-sequence models have provided a viable new approach f...
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04/03/2017
Online and Linear-Time Attention by Enforcing Monotonic Alignments
Recurrent neural network models with an attention mechanism have proven ...
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11/08/2016