Lizhen Qu

f-GANs in an Information Geometric Nutshell

2017 · arXiv (Cornell University)

Nowozin \textit{et al} showed last year how to extend the GAN \textit{principle} to all $f$-divergences. The approach is elegant but falls short of a full description of the supervised game, and says little about the …

Neural-Symbolic Commonsense Reasoner with Relation Predictors

2021

Farhad Moghimifar, Lizhen Qu, Terry Yue Zhuo, Gholamreza Haffari, Mahsa Baktashmotlagh. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: …

When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods

2022 · arXiv (Cornell University)

With increasing privacy concerns on data, recent studies have made significant progress using federated learning (FL) on privacy-sensitive natural language processing (NLP) tasks. Much literature suggests fully fine-tuning pre-trained language models (PLMs) in the FL …

RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations

2024 · arXiv (Cornell University)

Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. Remediating norm violations requires social awareness and cultural sensitivity of the nuances at play. To equip interactive …

IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models

2024 · arXiv (Cornell University)

Machine learning models have made incredible progress, but they still struggle when applied to examples from unseen domains. This study focuses on a specific problem of domain generalization, where a model is trained on one …

Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models

2024 · arXiv (Cornell University)

Large language models (LLMs) are typically fine-tuned on diverse and extensive datasets sourced from various origins to develop a comprehensive range of skills, such as writing, reasoning, chatting, coding, and more. Each skill has unique …

Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis

2024 · arXiv (Cornell University)

This work proposes a novel and simple sequential learning strategy to train models on videos and texts for multimodal sentiment analysis. To estimate sentiment polarities on unseen out-of-distribution data, we introduce a multimodal model that …

STransE: a novel embedding model of entities and relationships in knowledge bases

2016

Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, Mark Johnson. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.