Search Results for author:
Found 9 papers, 1 papers with code
Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees
In this study, we propose an inference trajectory optimization framework based on the preference data extracted from decision trees to address this limitation.
Wings: Learning Multimodal LLMs without Text-only Forgetting
Initially, image and text inputs are aligned with visual learners operating alongside the main attention, balancing focus on visual elements.
Parrot: Multilingual Visual Instruction Tuning
In this paper, we introduce Parrot, a novel method that utilizes textual guidance to drive visual token alignment at the language level.
Ovis: Structural Embedding Alignment for Multimodal Large Language Model
However, the misalignment between two embedding strategies in MLLMs -- the structural textual embeddings based on an embedding look-up table and the continuous embeddings generated directly by the vision encoder -- makes challenges for a more seamless fusion of visual and textual information.
TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Prompt
Then, a co-learning strategy with a dual-adapter module is designed to transfer visual knowledge from pseudo-visual prompt to text prompt, enhancing their visual representation abilities.
Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
SAM supports efficient training and real-time inference for user behavior sequences with lengths on the scale of thousands.
Iterative Memory Network for Long Sequential User Behavior Modeling in Recommender Systems
Extensive empirical studies show that our method outperforms various state-of-the-art sequential modeling methods on both public and industrial datasets for long sequential user behavior modeling.
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets.
Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution
Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation.
