no code implementations • 2 Apr 2025 • Bowen Cao, Deng Cai, Wai Lam
In-context learning (ICL) is critical for large language models (LLMs), but its effectiveness is constrained by finite context windows, particularly in ultra-long contexts.
1 code implementation • 26 Mar 2025 • Dingchen Yang, Bowen Cao, Anran Zhang, Weibo Gu, Winston Hu, Guang Chen
Multi-modal Large Langue Models (MLLMs) often process thousands of visual tokens, which consume a significant portion of the context window and impose a substantial computational burden.
no code implementations • 24 Jun 2024 • Deng Cai, Huayang Li, Tingchen Fu, Siheng Li, Weiwen Xu, Shuaiyi Li, Bowen Cao, Zhisong Zhang, Xinting Huang, Leyang Cui, Yan Wang, Lemao Liu, Taro Watanabe, Shuming Shi
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications.
2 code implementations • 8 Jun 2024 • Bowen Cao, Deng Cai, Zhisong Zhang, Yuexian Zou, Wai Lam
To address these limitations, we introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries and emphasizes the importance of using the worst prompt performance to gauge the lower bound of model performance.
1 code implementation • 21 Mar 2024 • Dingchen Yang, Bowen Cao, Guang Chen, Changjun Jiang
Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across various vision-language tasks.
1 code implementation • 27 Feb 2024 • Bowen Cao, Deng Cai, Leyang Cui, Xuxin Cheng, Wei Bi, Yuexian Zou, Shuming Shi
To address this, we propose to initialize the training oracles using linguistic heuristics and, more importantly, bootstrap the oracles through iterative self-reinforcement.
no code implementations • 19 Nov 2023 • Xuxin Cheng, Bowen Cao, Qichen Ye, Zhihong Zhu, Hongxiang Li, Yuexian Zou
Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
1 code implementation • 9 May 2023 • Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Bowen Cao, Jianhui Chang, Daxin Jiang, Jia Li
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities.
1 code implementation • 23 Feb 2023 • Bowen Cao, Qichen Ye, Weiyuan Xu, Yuexian Zou
Existing neighborhood aggregation strategies fail to capture either the short-term features or the long-term features of temporal graph attributes, leading to unsatisfactory model performance and even poor robustness and domain generality of the representation learning method.
1 code implementation • 23 Feb 2023 • Qichen Ye, Bowen Cao, Nuo Chen, Weiyuan Xu, Yuexian Zou
Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities.
1 code implementation • ECCV 2020 • Aishan Liu, Jiakai Wang, Xianglong Liu, Bowen Cao, Chongzhi Zhang, Hang Yu
To address the problem, this paper proposes a bias-based framework to generate class-agnostic universal adversarial patches with strong generalization ability, which exploits both the perceptual and semantic bias of models.