Search Results for author:
Found 12 papers, 8 papers with code
Improve Vision Language Model Chain-of-thought Reasoning
This work emphasizes the importance of incorporating detailed rationales in training and leveraging reinforcement learning to strengthen the reasoning capabilities of VLMs.
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM).
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries.
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence.
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification
We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification.
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM
To overcome these limitations, we introduce a novel method, namely GenCo, which leverages the strong generative power of LLMs to assist in training a smaller and more adaptable language model.
Exploiting Local and Global Features in Transformer-based Extreme Multi-label Text Classification
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.
Multi Label Text Classification
Multi-Label Text Classification
+1
Long-tailed Extreme Multi-label Text Classification with Generated Pseudo Label Descriptions
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 data scarce problem associated with the long tail of rare labels in highly skewed distributions.
Multi Label Text Classification
Multi-Label Text Classification
+3
Knowledge Embedding Based Graph Convolutional Network
Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN).
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
Learning Relation Entailment with Structured and Textual Information
We propose several methods that incorporate both structured and textual information to represent relations for this task.
Graph-Revised Convolutional Network
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications.
