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08/08/2023
Shepherd: A Critic for Language Model Generation
As large language models improve, there is increasing interest in techni...
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12/22/2022
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Recent work has shown that fine-tuning large pre-trained language models...
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12/19/2022
Training Trajectories of Language Models Across Scales
Scaling up language models has led to unprecedented performance gains, b...
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12/16/2022
MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation
Prompting large language models has enabled significant recent progress ...
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11/25/2022
Complementary Explanations for Effective In-Context Learning
Large language models (LLMs) have exhibited remarkable capabilities in l...
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05/03/2022
Improving In-Context Few-Shot Learning via Self-Supervised Training
Self-supervised pretraining has made few-shot learning possible for many...
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12/20/2021
Efficient Large Scale Language Modeling with Mixtures of Experts
Mixture of Experts layers (MoEs) enable efficient scaling of language mo...
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12/20/2021
Few-shot Learning with Multilingual Language Models
Large-scale autoregressive language models such as GPT-3 are few-shot le...
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12/16/2021
A Proposition-Level Clustering Approach for Multi-Document Summarization
Text clustering methods were traditionally incorporated into multi-docum...
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10/03/2021
Multi-Document Keyphrase Extraction: A Literature Review and the First Dataset
Keyphrase extraction has been comprehensively researched within the sing...
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09/23/2021
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
We introduce iFacetSum, a web application for exploring topical document...
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03/02/2021
Dual Reinforcement-Based Specification Generation for Image De-Rendering
Advances in deep learning have led to promising progress in inferring gr...
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03/02/2021
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
The progress in Query-focused Multi-Document Summarization (QMDS) has be...
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11/15/2020
DORB: Dynamically Optimizing Multiple Rewards with Bandits
Policy gradients-based reinforcement learning has proven to be a promisi...
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09/17/2020
Evaluating Interactive Summarization: an Expansion-Based Framework
Allowing users to interact with multi-document summarizers is a promisin...
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09/01/2020
SuperPAL: Supervised Proposition ALignment for Multi-Document Summarization and Derivative Sub-Tasks
Multi-document summarization (MDS) is a challenging task, often decompos...
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01/13/2020
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
Domain adaptation performance of a learning algorithm on a target domain...
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06/12/2019
Continual and Multi-Task Architecture Search
Architecture search is the process of automatically learning the neural ...
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04/11/2019
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Conducting a manual evaluation is considered an essential part of summar...
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04/08/2019
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning
Multi-task learning (MTL) has achieved success over a wide range of prob...
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09/12/2018
Game-Based Video-Context Dialogue
Current dialogue systems focus more on textual and speech context knowle...
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06/19/2018
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification
Sentence simplification aims to improve readability and understandabilit...
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05/28/2018
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation
An accurate abstractive summary of a document should contain all its sal...
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04/17/2018
Multi-Reward Reinforced Summarization with Saliency and Entailment
Abstractive text summarization is the task of compressing and rewriting ...
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08/07/2017
Reinforced Video Captioning with Entailment Rewards
Sequence-to-sequence models have shown promising improvements on the tem...
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04/24/2017
