Search Results for author:
Found 22 papers, 9 papers with code
Are Large Language Models Really Robust to Word-Level Perturbations?
Notably, we are surprised to discover that robustness tends to decrease as fine-tuning (SFT and RLHF) is conducted.
RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory
Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features.
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization
For example, even in the commonly adopted instructional videos, a speaker can sometimes refer to something that is not visually present in the current frame; and the semantic misalignment would only be more unpredictable for the raw videos from the internet.
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass
Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks.
Image Steganography based on Style Transfer
Image steganography is the art and science of using images as cover for covert communications.
RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals
To overcome such limitations, in this paper, we propose to utilize the radio signals, which can traverse obstacles and are unaffected by the lighting conditions to achieve silhouette segmentation.
RFGAN: RF-Based Human Synthesis
To the best of our knowledge, this is the first work to generate optical images based on RF signals.
ReasonBERT: Pre-trained to Reason with Distant Supervision
We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts.
Ranked #1 on
Semantic Parsing
on GraphQuestions
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model.
Ranked #3 on
Paraphrase Identification
on Quora Question Pairs
AgreeSum: Agreement-Oriented Multi-Document Summarization
Given the lack of existing datasets, we create a dataset for AgreeSum, and provide annotations on article-summary entailment relations for a subset of the clusters in the dataset.
NewsEmbed: Modeling News through Pre-trained Document Representations
Effectively modeling text-rich fresh content such as news articles at document-level is a challenging problem.
Training ELECTRA Augmented with Multi-word Selection
In this study, we present a new text encoder pre-training method that improves ELECTRA based on multi-task learning.
Quiz-Style Question Generation for News Stories
As a first step towards measuring news informedness at a scale, we study the problem of quiz-style multiple-choice question generation, which may be used to survey users about their knowledge of recent news.
Inertial and gravity wave transmissions near radiative-convective boundaries
Two configurations have been considered: waves propagate from the convective layer to the radiative stratified stable layer, or In this paper, we study inertial and gravity wave transmissions near radiative-convective boundaries on the {\it f}-plane.
Fluid Dynamics Solar and Stellar Astrophysics
TURL: Table Understanding through Representation Learning
In this paper, we present TURL, a novel framework that introduces the pre-training/fine-tuning paradigm to relational Web tables.
Ranked #1 on
Column Type Annotation
on WikipediaGS-CTA
A Generative Approach to Titling and Clustering Wikipedia Sections
We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
Generating Representative Headlines for News Stories
In this work, we study the problem of generating representative headlines for news stories.
CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese.
Chinese Named Entity Recognition
named-entity-recognition
+2
Personalized Fashion Design
Fashion recommendation is the task of suggesting a fashion item that fits well with a given item.
Generating Titles for Web Tables
This is accomplished by extracting many text snippets that have potentially relevant information to the table, encoding them into an input sequence, and using both copy and generation mechanisms in the decoder to balance relevance and readability of the generated title.
Investigating Rumor News Using Agreement-Aware Search
We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences.
