Search Results for author:
Found 29 papers, 17 papers with code
DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
We conceptualize these challenges as the "Trilemma of Cardinality Estimation", where learned cardinality estimation methods struggle to balance generality, accuracy, and updatability.
Collaborative Stance Detection via Small-Large Language Model Consistency Verification
However, heavily relying on LLMs for stance detection, regardless of the cost, is impractical for real-world social media monitoring systems that require vast data analysis.
from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors
Current studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks.
Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models
We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our meticulously collected system domain data.
Na'vi or Knave: Jailbreaking Language Models via Metaphorical Avatars
In our study, we introduce a novel attack framework that exploits the imaginative capacity of LLMs to achieve jailbreaking, the J\underline{\textbf{A}}ilbreak \underline{\textbf{V}}ia \underline{\textbf{A}}dversarial Me\underline{\textbf{TA}} -pho\underline{\textbf{R}} (\textit{AVATAR}).
MERLIN: Multi-stagE query performance prediction for dynamic paRallel oLap pIpeliNe
High-performance OLAP database technology has emerged with the growing demand for massive data analysis.
Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models
We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings.
Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models
The vast amount of biomedical information available today presents a significant challenge for investigators seeking to digest, process, and understand these findings effectively.
CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making
The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency and accessibility of medical care.
Xmodel-LM Technical Report
We introduce Xmodel-LM, a compact and efficient 1. 1B language model pre-trained on around 2 trillion tokens.
Harmonic and Interharmonic Detection in Power Systems Based on Fractal-Optimized Variational Mode Decomposition
The proposed method introduces a parameter determination approach based on the minimum Fractal box dimension (FBD) of Variational Mode Decomposition (VMD) components, aiming to address the issue of manual determination of VMD decomposition layers in advance.
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames
It aims to summarize a long video walkthrough of a scene into a small set of frames that are spatially diverse in the scene, which has many impotant applications, such as in surveillance, real estate, and robotics.
Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash
In this paper, we propose an attack type robust face anti-spoofing framework under light flash, called ATR-FAS.
Duet: efficient and scalable hybriD neUral rElation undersTanding
Although both data-driven and hybrid methods are proposed to avoid this problem, most of them suffer from high training and estimation costs, limited scalability, instability, and long-tail distribution problems on high-dimensional tables, which seriously affects the practical application of learned cardinality estimators.
A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation
To further illustrate the commercial value of our approach, we conduct experiments on three generation tasks in real-world advertisements applications.
Factorisation-based Image Labelling
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging.
FastSeq: Make Sequence Generation Faster
Transformer-based models have made tremendous impacts in natural language generation.
EL-Attention: Memory Efficient Lossless Attention for Generation
Transformer model with multi-head attention requires caching intermediate results for efficient inference in generation tasks.
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation.
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
GLGE: A New General Language Generation Evaluation Benchmark
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).
ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine
We build a dataset from a real-word sponsored search engine and carry out experiments to analyze different generative retrieval models.
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.
Index Selection for NoSQL Database with Deep Reinforcement Learning
We propose a new approach of NoSQL database index selection.
RikiNet: Reading Wikipedia Pages for Natural Question Answering
The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner.
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation
Furthermore, we propose a simple and effective method to mine the keyphrases of interest in the news article and build a first large-scale keyphrase-aware news headline corpus, which contains over 180K aligned triples of $<$news article, headline, keyphrase$>$.
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
Ranked #6 on
Question Generation
on SQuAD1.1
(using extra training data)
