3 citations · 17 across the 24 of their papers we have counts for
25 papers
Skim and Skip: Hierarchical Adaptive Inference for Efficient Multimodal Retrieval
Meng Gao, Yizhen Zhang, Yang Ding +10
Universal multimodal retrieval (UMR) increasingly adopts multimodal large language models (MLLMs) as unified embedding backbones, but their strong retrieval performance comes at su…
SafeCap: Improving LVLM Safety with Image Captioning Reinforcement Learning
Caoyuan Ma, Wenpu Liu, Weichu Xie +12
Large vision-language models (LVLMs) remain vulnerable to jailbreak attacks that exploit visual inputs to bypass safety alignment inherited from their language backbones. We propos…
Simple-OPD: Demystifying Warm-up for On-policy Distillation
Tao Liu, Taiqiang Wu, Mao Zheng +5
On-policy distillation (OPD) trains a student on its own rollouts with token-level supervision from teacher models, but its effectiveness can depend strongly on the warm-up stage b…
From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs
Runze Jiang, Taiqiang Wu, Yan Wang +2
When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reliability bottleneck. Existing…
A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms
Kejin Yu, Yuhan Sun, Taiqiang Wu +5
The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit in robust and generalizabl…
LINA: Linear Autoregressive Image Generative Models with Continuous Tokens
Jiahao Wang, Ting Pan, Haoge Deng +4
Autoregressive models with continuous tokens form a promising paradigm for visual generation, especially for text-to-image (T2I) synthesis, but they suffer from high computational…
ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection
Tao Liu, Taiqiang Wu, Runming Yang +3
Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional SFT often ignores the one-to-many…
PhyX: Does Your Model Have the "Wits" for Physical Reasoning?
Hui Shen, Taiqiang Wu, Qi Han +16
Existing benchmarks fail to capture a crucial aspect of intelligence: physical reasoning, the integrated ability to combine domain knowledge, symbolic reasoning, and understanding…
LiT: Delving into a Simple Linear Diffusion Transformer for Image Generation
Jiahao Wang, Ning Kang, Lewei Yao +12
In this paper, we investigate how to convert a pre-trained Diffusion Transformer (DiT) into a linear DiT, as its simplicity, parallelism, and efficiency for image generation. Throu…
LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models
Runming Yang, Taiqiang Wu, Jiahao Wang +4
Knowledge distillation (KD) has been a predominant method for compressing Large Language Models (LLMs). In this paper, we first revisit KD and Low-Rank Adaption (LoRA) and demonstr…
Autoregressive Models in Vision: A Survey
Jing Xiong, Gongye Liu, Lun Huang +17
Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in co…
MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers
Zebin Yang, Renze Chen, Taiqiang Wu +5
In this paper, we propose MCUBERT to enable language models like BERT on tiny microcontroller units (MCUs) through network and scheduling co-optimization. We observe the embedding…
A Survey on the Honesty of Large Language Models
Siheng Li, Cheng Yang, Taiqiang Wu +12
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to fai…
LoCa: Logit Calibration for Knowledge Distillation
Runming Yang, Taiqiang Wu, Yujiu Yang
Knowledge Distillation (KD), aiming to train a better student model by mimicking the teacher model, plays an important role in model compression. One typical way is to align the ou…
Mixture-of-Subspaces in Low-Rank Adaptation
Taiqiang Wu, Jiahao Wang, Zhe Zhao +1
In this paper, we introduce a subspace-inspired Low-Rank Adaptation (LoRA) method, which is computationally efficient, easy to implement, and readily applicable to large language,…
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
Chufan Shi, Cheng Yang, Xinyu Zhu +6
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activat…
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models
Taiqiang Wu, Chaofan Tao, Jiahao Wang +3
Kullback-Leiber divergence has been widely used in Knowledge Distillation (KD) to compress Large Language Models (LLMs). Contrary to prior assertions that reverse Kullback-Leibler…
Recouple Event Field via Probabilistic Bias for Event Extraction
Xingyu Bai, Taiqiang Wu, Han Guo +7
Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs). However, existing PL…
Weight-Inherited Distillation for Task-Agnostic BERT Compression
Taiqiang Wu, Cheng Hou, Shanshan Lao +4
Knowledge Distillation (KD) is a predominant approach for BERT compression. Previous KD-based methods focus on designing extra alignment losses for the student model to mimic the b…
RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer
Jiahao Wang, Songyang Zhang, Yong Liu +6
This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs),…
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
Taiqiang Wu, Zhe Zhao, Jiahao Wang +4
Distilling high-accuracy Graph Neural Networks (GNNs) to low-latency multilayer perceptions (MLPs) on graph tasks has become a hot research topic. However, conventional MLP learnin…
SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis
Chengze Yu, Taiqiang Wu, Jiayi Li +2
Aspect-based Sentiment Analysis (ABSA) is a sentiment analysis task at fine-grained level. Recently, generative frameworks have attracted increasing attention in ABSA due to their…
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
Zhe Zhao, Yudong Li, Cheng Hou +23
Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities a…
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
Taiqiang Wu, Xingyu Bai, Weigang Guo +3
Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods…