Search Results for author: Zifeng Wang

Found 47 papers, 24 papers with code

CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation

no code implementations8 Jun 2024 I-Hung Hsu, Zifeng Wang, Long T. Le, Lesly Miculicich, Nanyun Peng, Chen-Yu Lee, Tomas Pfister

Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources.

Open-Domain Question Answering

CodecLM: Aligning Language Models with Tailored Synthetic Data

no code implementations8 Apr 2024 Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister

To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs.

Instruction Following

ADAPT to Robustify Prompt Tuning Vision Transformers

no code implementations19 Mar 2024 Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer Dy

The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks.

Adversarial Defense

TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale

no code implementations15 Mar 2024 Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia, Jimeng Sun, Jiawei Han

To overcome this, we introduce TriSum, a framework for distilling LLMs' text summarization abilities into a compact, local model.

Text Summarization

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models

1 code implementation16 Feb 2024 Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, Jiawei Han

The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs).

Relation Relation Extraction +1

PILOT: Legal Case Outcome Prediction with Case Law

no code implementations28 Jan 2024 Lang Cao, Zifeng Wang, Cao Xiao, Jimeng Sun

We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.

Decision Making Retrieval

UniPredict: Large Language Models are Universal Tabular Classifiers

no code implementations5 Oct 2023 Ruiyu Wang, Zifeng Wang, Jimeng Sun

Specifically, we train a single LLM on an aggregation of 169 tabular datasets with diverse targets and compare its performance against baselines that are trained on each dataset separately.

Few-Shot Learning

BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs

1 code implementation5 Oct 2023 Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, Rishita Anubhai

Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.

Cross-Modal Retrieval Domain Generalization +3

CITING: Large Language Models Create Curriculum for Instruction Tuning

no code implementations4 Oct 2023 Tao Feng, Zifeng Wang, Jimeng Sun

Specifically, we employ a teacher LLM to create a curriculum for instruction tuning of the student LLM, namely Curriculum Instruction TunING (CITING).

MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models

1 code implementation17 Aug 2023 Yilin Wen, Zifeng Wang, Jimeng Sun

Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks.

Decision Making Hallucination +5

Matching Patients to Clinical Trials with Large Language Models

1 code implementation27 Jul 2023 Qiao Jin, Zifeng Wang, Charalampos S. Floudas, Fangyuan Chen, Changlin Gong, Dara Bracken-Clarke, Elisabetta Xue, Yifan Yang, Jimeng Sun, Zhiyong Lu

Given a patient note, TrialGPT predicts the patient's eligibility on a criterion-by-criterion basis and then consolidates these predictions to assess the patient's eligibility for the target trial.

Language Modelling Large Language Model

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)

no code implementations26 May 2023 Ruoxi Sun, Sercan Ö. Arik, Alex Muzio, Lesly Miculicich, Satya Gundabathula, Pengcheng Yin, Hanjun Dai, Hootan Nakhost, Rajarishi Sinha, Zifeng Wang, Tomas Pfister

Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data.

Data Augmentation In-Context Learning +3

MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement

1 code implementation20 May 2023 Zifeng Wang, Chufan Gao, Cao Xiao, Jimeng Sun

Tabular data prediction has been employed in medical applications such as patient health risk prediction.

SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning

no code implementations7 Apr 2023 Zifeng Wang, Cao Xiao, Jimeng Sun

Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success.

Meta-Learning

Robust Meta Learning for Image based tasks

no code implementations30 Jan 2023 Penghao Jiang, Xin Ke, Zifeng Wang, Chunxi Li

However, learning such a model is not possible in standard machine learning frameworks as the distribution of the test data is unknown.

Meta-Learning

Invariant Meta Learning for Out-of-Distribution Generalization

no code implementations26 Jan 2023 Penghao Jiang, Ke Xin, Zifeng Wang, Chunxi Li

Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data.

Meta-Learning Out-of-Distribution Generalization

QueryForm: A Simple Zero-shot Form Entity Query Framework

no code implementations14 Nov 2022 Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.

document understanding Transfer Learning

MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

1 code implementation18 Oct 2022 Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun

Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.

Contrastive Learning Retrieval +1

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning

1 code implementation11 Oct 2022 Zifeng Wang, Jimeng Sun

Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications.

Attribute Imputation +1

SparCL: Sparse Continual Learning on the Edge

1 code implementation20 Sep 2022 Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy

SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity.

Continual Learning

Artificial Intelligence for In Silico Clinical Trials: A Review

no code implementations16 Sep 2022 Zifeng Wang, Chufan Gao, Lucas M. Glass, Jimeng Sun

In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials.

Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision

1 code implementation29 Jun 2022 Zifeng Wang, Jimeng Sun

We propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns through self-supervision without annotating similar clinical trials.

Clinical Knowledge Retrieval

Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

1 code implementation12 Jan 2022 Batool Salehi, Guillem Reus-Muns, Debashri Roy, Zifeng Wang, Tong Jian, Jennifer Dy, Stratis Ioannidis, Kaushik Chowdhury

Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times.

Edge-computing

Learning to Prompt for Continual Learning

4 code implementations CVPR 2022 Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.

Class Incremental Learning Image Classification

SurvTRACE: Transformers for Survival Analysis with Competing Events

1 code implementation2 Oct 2021 Zifeng Wang, Jimeng Sun

In medicine, survival analysis studies the time duration to events of interest such as mortality.

Multi-Task Learning Selection bias +1

PAC-Bayes Information Bottleneck

1 code implementation ICLR 2022 Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng

Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.

Deep Bayesian Unsupervised Lifelong Learning

1 code implementation13 Jun 2021 Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer Dy

We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations.

Bayesian Inference

Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness

1 code implementation NeurIPS 2021 Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy

We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier.

Adversarial Robustness

Lifelong Learning based Disease Diagnosis on Clinical Notes

1 code implementation27 Feb 2021 Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen, Shao-Lun Huang, Yefeng Zheng

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.

Open-World Class Discovery with Kernel Networks

1 code implementation13 Dec 2020 Zifeng Wang, Batool Salehi, Andrey Gritsenko, Kaushik Chowdhury, Stratis Ioannidis, Jennifer Dy

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples.

Learn-Prune-Share for Lifelong Learning

1 code implementation13 Dec 2020 Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang, Jennifer Dy, Stratis Ioannidis

In lifelong learning, we wish to maintain and update a model (e. g., a neural network classifier) in the presence of new classification tasks that arrive sequentially.

Instance-wise Feature Grouping

no code implementations NeurIPS 2020 Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy

Moreover, the features that belong to each group, and the important feature groups may vary per sample.

General Classification

Finding Influential Instances for Distantly Supervised Relation Extraction

no code implementations COLING 2022 Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.

Relation Relation Extraction

Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

no code implementations6 Sep 2020 Zifeng Wang, Rui Wen, Xi Chen, Shilei Cao, Shao-Lun Huang, Buyue Qian, Yefeng Zheng

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).

Graph Representation Learning Retrieval

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

1 code implementation NeurIPS 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.

counterfactual Recommendation Systems

On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond

no code implementations25 Jan 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang

Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk.

counterfactual Fairness

Less Is Better: Unweighted Data Subsampling via Influence Function

1 code implementation3 Dec 2019 Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, Shao-Lun Huang

In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.

General Classification Image Classification +2

Streaming Adaptive Nonparametric Variational Autoencoder

no code implementations7 Jun 2019 Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer G. Dy

We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.

Clustering Feature Engineering +1

Collaborative Deep Reinforcement Learning for Multi-Object Tracking

no code implementations ECCV 2018 Liangliang Ren, Jiwen Lu, Zifeng Wang, Qi Tian, Jie zhou

To address this, we develop a deep prediction-decision network in our C-DRL, which simultaneously detects and predicts objects under a unified network via deep reinforcement learning.

Multi-Object Tracking Object +2

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