7 citations · 21 across the 11 of their papers we have counts for
11 papers
Improve Vision Language Model Chain-of-thought Reasoning
Ruohong Zhang, Bowen Zhang, Yanghao Li +6
Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes lack robust CoT r…
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
Ruohong Zhang, Liangke Gui, Zhiqing Sun +8
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However,…
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
Ruohong Zhang, Luyu Gao, Chen Zheng +6
Large Language Models (LLMs), despite their great power in language generation, often encounter challenges when dealing with intricate and knowledge-demanding queries in specific d…
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
Xuhui Zhou, Hao Zhu, Leena Mathur +8
Humans are social beings; we pursue social goals in our daily interactions, which is a crucial aspect of social intelligence. Yet, AI systems' abilities in this realm remain elusiv…
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification
Yau-Shian Wang, Ta-Chung Chi, Ruohong Zhang +1
We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural…
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM
Ruohong Zhang, Yau-Shian Wang, Yiming Yang
The remarkable performance of large language models (LLMs) in zero-shot language understanding has garnered significant attention. However, employing LLMs for large-scale inference…
Long-tailed Extreme Multi-label Text Classification with Generated Pseudo Label Descriptions
Ruohong Zhang, Yau-Shian Wang, Yiming Yang +3
Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe da…
Exploiting Local and Global Features in Transformer-based Extreme Multi-label Text Classification
Ruohong Zhang, Yau-Shian Wang, Yiming Yang +2
Extreme multi-label text classification (XMTC) is the task of tagging each document with the relevant labels from a very large space of predefined categories. Recently, large pre-t…
Knowledge Embedding Based Graph Convolutional Network
Donghan Yu, Yiming Yang, Ruohong Zhang +1
Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN). How to effectively leverage the rich structural information in complex graph…
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
Ruohong Zhang, Yu Hao, Donghan Yu +3
Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the d…
Graph-Revised Convolutional Network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang +2
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linka…