2 citations · 5 across the 35 of their papers we have counts for
8 papers · 1 filter
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation
Yuhang Liu, Xueyu Hu, Shengyu Zhang +3
Retrieval-Augmented Generation (RAG) has proven to be an effective method for mitigating hallucination issues inherent in large language models (LLMs). Previous approaches typicall…
Classifier-guided Gradient Modulation for Enhanced Multimodal Learning
Zirun Guo, Tao Jin, Jingyuan Chen +1
Multimodal learning has developed very fast in recent years. However, during the multimodal training process, the model tends to rely on only one modality based on which it could l…
AutoGeo: Automating Geometric Image Dataset Creation for Enhanced Geometry Understanding
Zihan Huang, Tao Wu, Wang Lin +3
With the rapid advancement of large language models, there has been a growing interest in their capabilities in mathematical reasoning. However, existing research has primarily foc…
Semantic Alignment for Multimodal Large Language Models
Tao Wu, Mengze Li, Jingyuan Chen +6
Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly…
Semantic Codebook Learning for Dynamic Recommendation Models
Zheqi Lv, Shaoxuan He, Tianyu Zhan +5
Dynamic sequential recommendation (DSR) can generate model parameters based on user behavior to improve the personalization of sequential recommendation under various user preferen…
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation
Kairui Fu, Shengyu Zhang, Zheqi Lv +2
Due to the continuously improving capabilities of mobile edges, recommender systems start to deploy models on edges to alleviate network congestion caused by frequent mobile reques…
MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning
Zhenlong Dai, Chang Yao, WenKang Han +3
Large Language Models (LLMs) have demonstrated great potential for assisting developers in their daily development. However, most research focuses on generating correct code, how t…
Non-confusing Generation of Customized Concepts in Diffusion Models
Wang Lin, Jingyuan Chen, Jiaxin Shi +8
We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs). It becomes even more pronounced in…