Search Results for author:
Found 40 papers, 13 papers with code
Position Engineering: Boosting Large Language Models through Positional Information Manipulation
Position engineering thus represents a promising new strategy for exploiting the capabilities of large language models.
LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation
This study proposes a novel evaluation framework using large language models (LLMs) to compare radiology reports for assessment.
DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering
The application of natural language processing models to PDF documents is pivotal for various business applications yet the challenge of training models for this purpose persists in businesses due to specific hurdles.
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks
We construct the distillation dataset via sampling sentences from language model pre-training datasets (e. g., OpenWebText in our implementation) and prompting an LLM to identify the typed spans of "important information".
LDB: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step
Large language models (LLMs) are leading significant progress in code generation.
Ranked #2 on
Code Generation
on HumanEval
Answer is All You Need: Instruction-following Text Embedding via Answering the Question
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions.
The Application of ChatGPT in Responding to Questions Related to the Boston Bowel Preparation Scale
This study aims to assess the accuracy and consistency of ChatGPT in using the Boston Bowel Preparation Scale (BBPS) for colonoscopy assessment.
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts.
Ranked #3 on
Table-based Fact Verification
on TabFact
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features
Detecting anomalies in fundus images through unsupervised methods is a challenging task due to the similarity between normal and abnormal tissues, as well as their indistinct boundaries.
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
Furthermore, we conducted a case study based on outbreak case data from the state of Colorado and showed that the timely detection of outbreaks could have been improved by up to 224% using TLGRF when compared to the decisions made by Colorado's Department of Health and Environment (CDPHE).
Large Language Model based Long-tail Query Rewriting in Taobao Search
In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue.
EmojiLM: Modeling the New Emoji Language
With the rapid development of the internet, online social media welcomes people with different backgrounds through its diverse content.
PAGE: Equilibrate Personalization and Generalization in Federated Learning
Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server).
LMDX: Language Model-based Document Information Extraction and Localization
Large Language Models (LLM) have revolutionized Natural Language Processing (NLP), improving state-of-the-art on many existing tasks and exhibiting emergent capabilities.
Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation
Existing code evaluation benchmark and datasets focus on crafting small tasks such as programming questions in coding interviews, which however deviates from the problem that developers would ask LLM for real-world coding help.
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation
Recent advances of incorporating layout information, typically bounding box coordinates, into pre-trained language models have achieved significant performance in entity recognition from document images.
Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path
In this paper, we propose a new approach, ReXMiner, for zero-shot relation extraction in web mining.
A Computational Model of Children's Learning and Use of Probabilities Across Different Ages
Recent empirical work has shown that human children are adept at learning and reasoning with probabilities.
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training
E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries.
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement
We first present an empirical analysis on the problems and opportunities of training MoE models, which motivates us to overcome the routing imbalance and fluctuation problems by a dynamic expert management and device placement mechanism.
DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization
Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss.
MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search
Given a user query, the retrieval phase returns a subset of candidate products for the following ranking phase.
Improved Differential-neural Cryptanalysis for Round-reduced Simeck32/64
Inspired by this framework, we develop the Inception neural network that is compatible with the round function of Simeck to improve the accuracy of the neural distinguishers, thus improving the accuracy of (9-12)-round neural distinguishers for Simeck32/64.
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective
The experimental evaluations manifest the superiority of SAT over other state-of-the-art AT mechanisms in defending against adversarial attacks against both output and intermediate layers.
2D Human Pose Estimation with Explicit Anatomical Keypoints Structure Constraints
Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process.
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding
In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features.
VRDU: A Benchmark for Visually-rich Document Understanding
Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry.
A Neural Model of Number Comparison with Surprisingly Robust Generalization
We propose a relatively simple computational neural-network model of number comparison.
Tutel: Adaptive Mixture-of-Experts at Scale
On efficiency, Flex accelerates SwinV2-MoE, achieving up to 1. 55x and 2. 11x speedup in training and inference over Fairseq, respectively.
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition
Fine-tuning pre-trained language models has recently become a common practice in building NLP models for various tasks, especially few-shot tasks.
Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework
To overcome the data limitation, we propose to leverage the label surface names to better inform the model of the target entity type semantics and also embed the labels into the spatial embedding space to capture the spatial correspondence between regions and labels.
A Note on "Optimum Sets of Interference-Free Sequences With Zero Autocorrelation Zone"
In this paper, a simple construction of interference-free zero correlation zone (IF-ZCZ) sequence sets is proposed by well designed finite Zak transform lattice tessellation.
Bilateral-ViT for Robust Fovea Localization
The fovea is an important anatomical landmark of the retina.
LayoutReader: Pre-training of Text and Layout for Reading Order Detection
Reading order detection is the cornerstone to understanding visually-rich documents (e. g., receipts and forms).
Ranked #2 on
Reading Order Detection
on ReadingBank
Document Layout Analysis
Optical Character Recognition (OCR)
+1
GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding
Forms are a common type of document in real life and carry rich information through textual contents and the organizational structure.
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment
Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+1
Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests
Rapid and accurate detection of community outbreaks is critical to address the threat of resurgent waves of COVID-19.
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding
The table detection and handcrafted features in previous works cannot apply to all forms because of their requirements on formats.
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis
Enlightened by recent success of Transformer in the area of machine translation, we propose a new fusion method, TransModality, to address the task of multimodal sentiment analysis.
Ranked #1 on
Multimodal Sentiment Analysis
on CMU-MOSI
(F1-score (Weighted) metric)
