1 citations · 1 across the 13 of their papers we have counts for
13 papers
Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning
Hongxing Li, Xiufeng Huang, Dingming Li +11
Fine-grained visual reasoning remains challenging for vision-language models, especially when small but critical visual cues are buried in high-resolution images. Existing approach…
Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety
Ting Ma, Xiufeng Huang, Benlei Cui +43
As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safet…
MultiHashFormer: Hash-based Generative Language Models
Huiyin Xue, Atsuki Yamaguchi, Nikolaos Aletras
Language models (LMs) represent tokens using embedding matrices that scale linearly with the vocabulary size. To constrain the parameter footprint, prior work proposes hashing many…
Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts
Haolei Xu, Haiwen Hong, Hongxing Li +7
Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking:…
QExplorer: Large Language Model Based Query Extraction for Toxic Content Exploration
Shaola Ren, Li Ke, Longtao Huang +2
Automatically extracting effective queries is challenging in information retrieval, especially in toxic content exploration, as such content is likely to be disguised. With the rec…
Towards Rehearsal-Free Multilingual ASR: A LoRA-based Case Study on Whisper
Tianyi Xu, Kaixun Huang, Pengcheng Guo +4
Pre-trained multilingual speech foundation models, like Whisper, have shown impressive performance across different languages. However, adapting these models to new or specific lan…
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
Taolin Zhang, Dongyang Li, Qizhou Chen +5
Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the halluci…
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning
Dongyang Li, Taolin Zhang, Longtao Huang +3
Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via sel…
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding
Dongyang Li, Taolin Zhang, Jiali Deng +4
Cross-lingual representation learning transfers knowledge from resource-rich data to resource-scarce ones to improve the semantic understanding abilities of different languages. Ho…
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models
Dongyang Li, Junbing Yan, Taolin Zhang +5
Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to us…
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
Taolin Zhang, Qizhou Chen, Dongyang Li +5
Recently, while large language models (LLMs) have demonstrated impressive results, they still suffer from hallucination, i.e., the generation of false information. Model editing is…
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
Qizhou Chen, Taolin Zhang, Xiaofeng He +4
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining. Lifelong model editing is the most challenging…
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
Junbing Yan, Chengyu Wang, Taolin Zhang +5
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding. Previous language models facilitated knowledge acquisition by incorporating knowled…