no code implementations • 1 Apr 2024 • Bohao Yang, Kun Zhao, Chen Tang, Liang Zhan, Chenghua Lin
Trainable evaluation metrics are commonly trained with true positive and randomly selected negative responses, resulting in a tendency for them to assign a higher score to the responses that share higher content similarity with a given context.
1 code implementation • 24 Jan 2024 • Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines.
no code implementations • 22 Sep 2023 • Bohao Yang, Chen Tang, Kun Zhao, Chenghao Xiao, Chenghua Lin
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks.
1 code implementation • 19 Sep 2023 • Bohao Yang, Chen Tang, Chenghua Lin
In this paper, We propose a novel framework that models dialogues between patients and healthcare professionals using AMR graphs, where the neural networks incorporate textual and graphical knowledge with a dual attention mechanism.
1 code implementation • 26 May 2023 • Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, Xiaohui Cui
The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i. e., there may be multiple suitable responses which differ in semantics for a given conversational context.
1 code implementation • 5 Nov 2022 • Yizhi Li, Ge Zhang, Bohao Yang, Chenghua Lin, Shi Wang, Anton Ragni, Jie Fu
In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups.
no code implementations • 11 Jul 2022 • Owen Millwood, Jack Miskelly, Bohao Yang, Prosanta Gope, Elif Kavun, Chenghua Lin
As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage.