Liang Ding

Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis

2022 · arXiv (Cornell University)

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current …

BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation

2022 · arXiv (Cornell University)

Data augmentations (DA) are the cores to achieving robust sequence-to-sequence learning on various natural language processing (NLP) tasks. However, most of the DA approaches force the decoder to make predictions conditioned on the perturbed input …

Representing Additive Gaussian Processes by Sparse Matrices

2023 · arXiv (Cornell University)

Among generalized additive models, additive Matérn Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure and stochastic differential equation representation, back-fitting-based algorithms can reduce the time …

Exploring Sparsity in Graph Transformers

2023 · arXiv (Cornell University)

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, especially in resource-constrained environments. Therefore, in this paper, we explore the feasibility …

Towards Alleviating Text-to-Image Retrieval Hallucination for CLIP in Zero-shot Learning

2024 · arXiv (Cornell University)

Pretrained cross-modal models, for instance, the most representative CLIP, have recently led to a boom in using pre-trained models for cross-modal zero-shot tasks, considering the generalization properties. However, we analytically discover that CLIP suffers from …

AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration

2025

Multi-agent systems (MAS) based on large language models (LLMs) have demonstrated significant potential in collaborative problemsolving.However, they still face substantial challenges of low communication efficiency and suboptimal task performance, making the careful design of the …

Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

2023 · arXiv (Cornell University)

Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. Several prior studies have shown that ChatGPT attains remarkable generation ability compared with existing models. However, the quantitative …