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07/05/2023
SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
Autoregressive large language models (LLMs) have made remarkable progres...
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06/19/2023
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding
State-of-the-art few-shot learning (FSL) methods leverage prompt-based f...
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06/05/2023
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Recent research has focused on enhancing the capability of smaller model...
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05/24/2023
GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions
Generalization to unseen tasks is an important ability for few-shot lear...
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05/03/2023
A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training
Modern Natural Language Generation (NLG) models come with massive comput...
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10/31/2022
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
Standard fine-tuning of large pre-trained language models (PLMs) for dow...
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10/14/2022
AutoMoE: Neural Architecture Search for Efficient Sparsely Activated Transformers
Neural architecture search (NAS) has demonstrated promising results on i...
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05/24/2022
AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models
Fine-tuning large-scale pre-trained language models to downstream tasks ...
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04/16/2022
Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners
Traditional multi-task learning (MTL) methods use dense networks that us...
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01/29/2022
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Knowledge distillation (KD) methods compress large models into smaller s...
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11/04/2021
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
Most recent progress in natural language understanding (NLU) has been dr...
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10/16/2021
What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression
Recent works have focused on compressing pre-trained language models (PL...
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10/12/2021
LiST: Lite Self-training Makes Efficient Few-shot Learners
We present a new method LiST for efficient fine-tuning of large pre-trai...
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09/17/2021
Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU
While pre-trained language models have obtained state-of-the-art perform...
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06/23/2021
Fairness via Representation Neutralization
Existing bias mitigation methods for DNN models primarily work on learni...
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06/08/2021
XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation
While deep and large pre-trained models are the state-of-the-art for var...
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04/16/2021
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
The combination of multilingual pre-trained representations and cross-li...
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04/12/2021
Self-Training with Weak Supervision
State-of-the-art deep neural networks require large-scale labeled traini...
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10/07/2020
Adaptive Self-training for Few-shot Neural Sequence Labeling
Neural sequence labeling is an important technique employed for many Nat...
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06/27/2020
Uncertainty-aware Self-training for Text Classification with Few Labels
Recent success of large-scale pre-trained language models crucially hing...
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05/26/2020
Learning with Weak Supervision for Email Intent Detection
Email remains one of the most frequently used means of online communicat...
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05/18/2020
Product Insights: Analyzing Product Intents in Web Search
Web search engines are frequently used to access information about produ...
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05/05/2020
Smart To-Do : Automatic Generation of To-Do Items from Emails
Intelligent features in email service applications aim to increase produ...
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05/02/2020
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer
Multilingual representations embed words from many languages into a sing...
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04/12/2020
TinyMBERT: Multi-Stage Distillation Framework for Massive Multi-lingual NER
Deep and large pre-trained language models are the state-of-the-art for ...
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10/14/2019
STANCY: Stance Classification Based on Consistency Cues
Controversial claims are abundant in online media and discussion forums....
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10/04/2019
Distilling Transformers into Simple Neural Networks with Unlabeled Transfer Data
Recent advances in pre-training huge models on large amounts of text thr...
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05/15/2019
GhostLink: Latent Network Inference for Influence-aware Recommendation
Social influence plays a vital role in shaping a user's behavior in onli...
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04/12/2019
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
In this paper, we consider advancing web-scale knowledge extraction and ...
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09/17/2018
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Misinformation such as fake news is one of the big challenges of our soc...
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06/01/2018
OpenTag: Open Attribute Value Extraction from Product Profiles
Extraction of missing attribute values is to find values describing an a...
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07/26/2017
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities
One of the major hurdles preventing the full exploitation of information...
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05/07/2017
Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion
Online review communities are dynamic as users join and leave, adopt new...
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05/07/2017
Credible Review Detection with Limited Information using Consistency Analysis
Online reviews provide viewpoints on the strengths and shortcomings of p...
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05/07/2017
People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities
Media seems to have become more partisan, often providing a biased cover...
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05/06/2017
People on Drugs: Credibility of User Statements in Health Communities
Online health communities are a valuable source of information for patie...
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05/06/2017
Item Recommendation with Evolving User Preferences and Experience
Current recommender systems exploit user and item similarities by collab...
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05/06/2017