Search Results for author: Wanqi Yang

Found 14 papers, 2 papers with code

Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Language Models

1 code implementation22 Nov 2024 Wanqi Yang, Yanda Li, Meng Fang, Yunchao Wei, Tianyi Zhou, Ling Chen

We evaluate six state-of-the-art LLMs with voice interaction capabilities, including Gemini-1. 5-Pro, GPT-4o, and others, using three distinct evaluation methods on the CAA benchmark.

Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering

no code implementations25 Sep 2024 Wanqi Yang, Yanda Li, Meng Fang, Ling Chen

Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions.

Question Answering

AppAgent v2: Advanced Agent for Flexible Mobile Interactions

no code implementations5 Aug 2024 Yanda Li, Chi Zhang, Wanqi Yang, Bin Fu, Pei Cheng, Xin Chen, Ling Chen, Yunchao Wei

In the deployment phase, RAG technology enables efficient retrieval and update from this knowledge base, thereby empowering the agent to perform tasks effectively and accurately.

RAG

Continual Learning for Temporal-Sensitive Question Answering

no code implementations17 Jul 2024 Wanqi Yang, Yunqiu Xu, Yanda Li, Kunze Wang, Binbin Huang, Ling Chen

In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA).

Continual Learning Contrastive Learning +1

EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation

no code implementations4 Jul 2024 Wanqi Yang, Haoran Wang, Lei Wang, Ge Song, Yang Gao

However, current FS-UDA methods are still suffer from two issues: 1) the data from different domains can not be effectively aligned by few-shot labeled data due to the large domain gaps, 2) it is unstable and time-consuming to generalize to new FS-UDA tasks. To address this issue, we put forward a novel Efficient Meta Prompt Learning Framework for FS-UDA.

Bilevel Optimization Meta-Learning +1

Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention

no code implementations2 Jul 2024 Yuquan Xie, Wanqi Yang, Jinyu Wei, Ming Yang, Yang Gao

To address this issue, we propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains, and new education systems with limited data are considered target domains.

Domain Generalization Knowledge Tracing +1

Multi-level Reliable Guidance for Unpaired Multi-view Clustering

no code implementations1 Jul 2024 Like Xin, Wanqi Yang, Lei Wang, Ming Yang

In cross-view learning, reliable view guidance enhances the confidence of the cluster structures in other views.

Clustering Incomplete multi-view clustering

Unpaired Multi-view Clustering via Reliable View Guidance

no code implementations27 Apr 2024 Like Xin, Wanqi Yang, Lei Wang, Ming Yang

We assume that the view with a good cluster structure is the reliable view, which acts as a supervisor to guide the clustering of the other views.

Clustering Incomplete multi-view clustering

High-level semantic feature matters few-shot unsupervised domain adaptation

no code implementations5 Jan 2023 Lei Yu, Wanqi Yang, Shengqi Huang, Lei Wang, Ming Yang

However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain.

Few-Shot Learning Unsupervised Domain Adaptation +1

Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding

no code implementations6 Aug 2021 Shengqi Huang, Wanqi Yang, Lei Wang, Luping Zhou, Ming Yang

Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation.

Few-Shot Learning Image Classification +1

Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective

no code implementations31 Jul 2020 Yinghuan Shi, Wanqi Yang, Kim-Han Thung, Hao Wang, Yang Gao, Yang Pan, Li Zhang, Dinggang Shen

Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.

Class Distribution Alignment for Adversarial Domain Adaptation

no code implementations20 Apr 2020 Wanqi Yang, Tong Ling, Chengmei Yang, Lei Wang, Yinghuan Shi, Luping Zhou, Ming Yang

To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains.

General Classification Translation +1

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