Search Results for author: Xiaohan Bi

Found 6 papers, 6 papers with code

Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality

1 code implementation28 Mar 2024 Sishuo Chen, Lei LI, Shuhuai Ren, Rundong Gao, Yuanxin Liu, Xiaohan Bi, Xu sun, Lu Hou

Video paragraph captioning (VPC) involves generating detailed narratives for long videos, utilizing supportive modalities such as speech and event boundaries.

Data Augmentation Video Understanding

Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents

1 code implementation17 Feb 2024 Wenkai Yang, Xiaohan Bi, Yankai Lin, Sishuo Chen, Jie zhou, Xu sun

We first formulate a general framework of agent backdoor attacks, then we present a thorough analysis on the different forms of agent backdoor attacks.

Backdoor Attack Data Poisoning

Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter

1 code implementation21 May 2023 Yi Liu, Xiaohan Bi, Lei LI, Sishuo Chen, Wenkai Yang, Xu sun

However, as pre-trained language models (PLMs) continue to increase in size, the communication cost for transmitting parameters during synchronization has become a training speed bottleneck.

Clustering Federated Learning +2

Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features

2 code implementations30 Jan 2023 Sishuo Chen, Wenkai Yang, Xiaohan Bi, Xu sun

We find that: (1) no existing method behaves well in both settings; (2) fine-tuning PLMs on in-distribution data benefits detecting semantic shifts but severely deteriorates detecting non-semantic shifts, which can be attributed to the distortion of task-agnostic features.

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

Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks

1 code implementation14 Oct 2022 Sishuo Chen, Wenkai Yang, Zhiyuan Zhang, Xiaohan Bi, Xu sun

In this work, we take the first step to investigate the unconcealment of textual poisoned samples at the intermediate-feature level and propose a feature-based efficient online defense method.

backdoor defense Sentiment Analysis

Holistic Sentence Embeddings for Better Out-of-Distribution Detection

1 code implementation14 Oct 2022 Sishuo Chen, Xiaohan Bi, Rundong Gao, Xu sun

On the basis of the observations that token averaging and layer combination contribute to improving OOD detection, we propose a simple embedding approach named Avg-Avg, which averages all token representations from each intermediate layer as the sentence embedding and significantly surpasses the state-of-the-art on a comprehensive suite of benchmarks by a 9. 33% FAR95 margin.

Avg Out-of-Distribution Detection +4

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