9 papers
HKRAG: Holistic Knowledge Retrieval-Augmented Generation Over Visually-Rich Documents
Anyang Tong, Xiang Niu, ZhiPing Liu +4
Existing multimodal Retrieval-Augmented Generation (RAG) methods for visually rich documents (VRD) are often biased towards retrieving salient knowledge(e.g., prominent text and vi…
Understanding Network Behaviors through Natural Language Question-Answering
Mingzhe Xing, Chang Tian, Jianan Zhang +4
Modern large-scale networks introduce significant complexity in understanding network behaviors, increasing the risk of misconfiguration. Prior work proposed to understand network…
Controllable Stylistic Text Generation with Train-Time Attribute-Regularized Diffusion
Fan Zhou, Chang Tian, Tim Van de Cruys
Generating stylistic text with specific attributes is a key problem in controllable text generation. Recently, diffusion models have emerged as a powerful paradigm for both visual…
Automated Prompt Generation for Creative and Counterfactual Text-to-image Synthesis
Aleksa Jelaca, Ying Jiao, Chang Tian +1
Text-to-image generation has advanced rapidly with large-scale multimodal training, yet fine-grained controllability remains a critical challenge. Counterfactual controllability, d…
Structured Information for Improving Spatial Relationships in Text-to-Image Generation
Sander Schildermans, Chang Tian, Ying Jiao +1
Text-to-image (T2I) generation has advanced rapidly, yet faithfully capturing spatial relationships described in natural language prompts remains a major challenge. Prior efforts h…
Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning
Chang Tian, Matthew B. Blaschko, Mingzhe Xing +3
Reinforcement learning (RL) has become a key technique for enhancing the reasoning abilities of large language models (LLMs), with policy-gradient algorithms dominating the post-tr…
Using Causality for Enhanced Prediction of Web Traffic Time Series
Chang Tian, Mingzhe Xing, Zenglin Shi +3
Predicting web service traffic has significant social value, as it can be applied to various practical scenarios, including but not limited to dynamic resource scaling, load balanc…
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition
Chang Tian, Wenpeng Yin, Dan Li +1
Few-shot named entity recognition (NER) systems recognize entities using a few labeled training examples. The general pipeline consists of a span detector to identify entity spans…
A Generic Method for Fine-grained Category Discovery in Natural Language Texts
Chang Tian, Matthew B. Blaschko, Wenpeng Yin +3
Fine-grained category discovery using only coarse-grained supervision is a cost-effective yet challenging task. Previous training methods focus on aligning query samples with posit…