Search Results for author: Dongling Xiao

Found 9 papers, 5 papers with code

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

no code implementations14 Jun 2024 Jiawei Chen, Dingkang Yang, Tong Wu, Yue Jiang, Xiaolu Hou, Mingcheng Li, Shunli Wang, Dongling Xiao, Ke Li, Lihua Zhang

To bridge this gap, we introduce Med-HallMark, the first benchmark specifically designed for hallucination detection and evaluation within the medical multimodal domain.

Hallucination Medical Visual Question Answering +2

PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications

1 code implementation29 May 2024 Dingkang Yang, Jinjie Wei, Dongling Xiao, Shunli Wang, Tong Wu, Gang Li, Mingcheng Li, Shuaibing Wang, Jiawei Chen, Yue Jiang, Qingyao Xu, Ke Li, Peng Zhai, Lihua Zhang

In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery.

Diagnostic Domain Adaptation

Towards Multimodal Sentiment Analysis Debiasing via Bias Purification

no code implementations8 Mar 2024 Dingkang Yang, Mingcheng Li, Dongling Xiao, Yang Liu, Kun Yang, Zhaoyu Chen, Yuzheng Wang, Peng Zhai, Ke Li, Lihua Zhang

In the inference phase, given a factual multimodal input, MCIS imagines two counterfactual scenarios to purify and mitigate these biases.

counterfactual Counterfactual Inference +1

ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

6 code implementations26 Jan 2020 Dongling Xiao, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.

 Ranked #1 on Question Generation on SQuAD1.1 (using extra training data)

Abstractive Text Summarization Decoder +4

PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain

1 code implementation24 Sep 2019 Dongling Xiao, Chang Liu, Qi. Wang, Chao Wang, Xin Zhang

For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required.

Classification General Classification +2

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