Search Results for author: Xujiang Zhao

Found 24 papers, 10 papers with code

Pruning as a Domain-specific LLM Extractor

1 code implementation10 May 2024 Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen

Moreover, by efficiently approximating weight importance with the refined training loss on a domain-specific calibration dataset, we obtain a pruned model emphasizing generality and specificity.


Uncertainty Quantification for In-Context Learning of Large Language Models

1 code implementation15 Feb 2024 Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen

Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.

Hallucination In-Context Learning +1

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

no code implementations18 Oct 2023 Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao

In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision.

Question Answering Retrieval

Large Language Models Can Be Good Privacy Protection Learners

no code implementations3 Oct 2023 Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, Wei Cheng

To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.

Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments

no code implementations22 Sep 2023 Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen

This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features.

Causal Inference counterfactual +2

Adaptation Speed Analysis for Fairness-aware Causal Models

no code implementations31 Aug 2023 Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen

In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable.

Fairness Machine Translation +1

Multidimensional Uncertainty Quantification for Deep Neural Networks

no code implementations20 Apr 2023 Xujiang Zhao

In the first part of this thesis, we develop a general learning framework to quantify multiple types of uncertainties caused by different root causes, such as vacuity (i. e., uncertainty due to a lack of evidence) and dissonance (i. e., uncertainty due to conflicting evidence), for graph neural networks.

Decision Making Drug Discovery +4

Dynamic Prompting: A Unified Framework for Prompt Tuning

1 code implementation6 Mar 2023 Xianjun Yang, Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Petzold, Haifeng Chen

Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks.


Knowledge-enhanced Neural Machine Reasoning: A Review

no code implementations4 Feb 2023 Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao

Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications.

A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning

no code implementations12 Jun 2022 Zhen Guo, Zelin Wan, Qisheng Zhang, Xujiang Zhao, Feng Chen, Jin-Hee Cho, Qi Zhang, Lance M. Kaplan, Dong H. Jeong, Audun Jøsang

We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty.

Decision Making

Layer Adaptive Deep Neural Networks for Out-of-distribution Detection

1 code implementation1 Mar 2022 Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen

During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

SEED: Sound Event Early Detection via Evidential Uncertainty

no code implementations5 Feb 2022 Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen

Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.

Event Detection Sound Event Detection

Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation

1 code implementation EMNLP 2021 Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, Jinho D. Choi

Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages.

Cross-Lingual Transfer named-entity-recognition +4

RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning

1 code implementation NeurIPS 2021 KrishnaTeja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh Iyer

In this work, we propose RETRIEVE, a coreset selection framework for efficient and robust semi-supervised learning.

Multidimensional Uncertainty-Aware Evidential Neural Networks

1 code implementation26 Dec 2020 Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, Feng Chen

By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem.

Generative Adversarial Network Multi-class Classification +3

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning

no code implementations NeurIPS 2020 Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu

We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data.

Uncertainty Aware Semi-Supervised Learning on Graph Data

1 code implementation NeurIPS 2020 Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.

Node Classification Out of Distribution (OOD) Detection

How Out-of-Distribution Data Hurts Semi-Supervised Learning

1 code implementation7 Oct 2020 Xujiang Zhao, Killamsetty Krishnateja, Rishabh Iyer, Feng Chen

This work addresses the following question: How do out-of-distribution (OOD) data adversely affect semi-supervised learning algorithms?

Hyperparameter Optimization

Quantifying Classification Uncertainty using Regularized Evidential Neural Networks

no code implementations15 Oct 2019 Xujiang Zhao, Yuzhe Ou, Lance Kaplan, Feng Chen, Jin-Hee Cho

However, an ENN is trained as a black box without explicitly considering different types of inherent data uncertainty, such as vacuity (uncertainty due to a lack of evidence) or dissonance (uncertainty due to conflicting evidence).

Classification General Classification

Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data

1 code implementation12 Oct 2019 Xujiang Zhao, Feng Chen, Jin-Hee Cho

Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions.

Decision Making

Evidence-Aware Entropy Decomposition For Active Deep Learning

no code implementations25 Sep 2019 Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu

We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data.

Density Estimation

Uncertainty-Aware Prediction for Graph Neural Networks

no code implementations25 Sep 2019 Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

In this work, we propose a Bayesian deep learning framework reflecting various types of uncertainties for classification predictions by leveraging the powerful modeling and learning capabilities of GNNs.

Classification Node Classification +1

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