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05/27/2023
Fine-Tuning Language Models with Just Forward Passes
Fine-tuning language models (LMs) has yielded success on diverse downstr...
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05/24/2023
Enabling Large Language Models to Generate Text with Citations
Large language models (LLMs) have emerged as a widely-used tool for info...
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05/16/2023
What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning
Large language models (LLMs) exploit in-context learning (ICL) to solve ...
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04/25/2023
STM-UNet: An Efficient U-shaped Architecture Based on Swin Transformer and Multi-scale MLP for Medical Image Segmentation
Automated medical image segmentation can assist doctors to diagnose fast...
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11/10/2022
The CRINGE Loss: Learning what language not to model
Standard language model training employs gold human documents or human-h...
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10/15/2022
Transformer-based dimensionality reduction
Recently, Transformer is much popular and plays an important role in the...
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09/20/2022
Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models
Prompting, which casts downstream applications as language modeling task...
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05/17/2022
Recovering Private Text in Federated Learning of Language Models
Federated learning allows distributed users to collaboratively train a m...
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02/16/2022
Should You Mask 15
Masked language models conventionally use a masking rate of 15 belief th...
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12/16/2021
Ditch the Gold Standard: Re-evaluating Conversational Question Answering
Conversational question answering (CQA) systems aim to provide natural-l...
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05/20/2021
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction
Distantly supervised (DS) relation extraction (RE) has attracted much at...
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04/18/2021
SimCSE: Simple Contrastive Learning of Sentence Embeddings
This paper presents SimCSE, a simple contrastive learning framework that...
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12/31/2020
Making Pre-trained Language Models Better Few-shot Learners
The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot...
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12/09/2020
GRP: A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation
Rating prediction is a core problem in recommender systems to quantify u...
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12/08/2020
TADO: Time-varying Attention with Dual-Optimizer Model
The review-based recommender systems are commonly utilized to measure us...
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10/05/2020
Learning from Context or Names? An Empirical Study on Neural Relation Extraction
Neural models have achieved remarkable success on relation extraction (R...
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07/05/2020
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
This paper studies few-shot relation extraction, which aims at predictin...
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04/07/2020
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
Relational facts are an important component of human knowledge, which ar...
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11/13/2019
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Pre-trained language representation models (PLMs) learn effective langua...
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10/16/2019
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
We present FewRel 2.0, a more challenging task to investigate two aspect...
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09/28/2019
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
OpenNRE is an open-source and extensible toolkit that provides a unified...
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08/29/2019
