19 citations · 40 across the 13 of their papers we have counts for
14 papers
Balancing Performance and Diversity in GRPO Autoregressive Text-to-Image Post-Training
Yuanhao Chiang, Hongbo Duan, Chunru Yang +3
Autoregressive text-to-image (T2I) generation has recently advanced rapidly, yet aligning generated images with human preferences remains challenging. GRPO-style online reinforceme…
IdioLink: Retrieving Meaning Beyond Words Across Idiomatic and Literal Expressions
Kai Golan Hashiloni, Daniel Fadlon, Lior Livyatan +3
Idioms pose a fundamental challenge for language models, as their meaning cannot be inferred from surface form alone. Understanding such expressions, therefore, requires semantic a…
OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation
Jiahua Pei, Yi Liu, Guoping Pan +3
Object Goal Navigation (ObjectNav) refers to an agent navigating to an object in an unseen environment, which is an ability often required in the accomplishment of complex tasks. W…
Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks
Wentao Deng, Jiahuan Pei, Zhiwei Xu +3
A multi-agent system (MAS) enhances its capacity to solve complex natural language processing (NLP) tasks through collaboration among multiple agents, where consensus-seeking serve…
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality
Jiahuan Pei, Irene Viola, Haochen Huang +9
Autonomous artificial intelligence (AI) agents have emerged as promising protocols for automatically understanding the language-based environment, particularly with the exponential…
ExcluIR: Exclusionary Neural Information Retrieval
Wenhao Zhang, Mengqi Zhang, Shiguang Wu +5
Exclusion is an important and universal linguistic skill that humans use to express what they do not want. However, in information retrieval community, there is little research on…
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
Pengjie Ren, Chengshun Shi, Shiguang Wu +5
Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increa…
Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues
Wentao Deng, Jiahuan Pei, Zhaochun Ren +2
Answer selection in open-domain dialogues aims to select an accurate answer from candidates. Recent success of answer selection models hinges on training with large amounts of labe…
Transformer Uncertainty Estimation with Hierarchical Stochastic Attention
Jiahuan Pei, Cheng Wang, György Szarvas
Transformers are state-of-the-art in a wide range of NLP tasks and have also been applied to many real-world products. Understanding the reliability and certainty of transformer mo…
ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues
Guojun Yan, Jiahuan Pei, Pengjie Ren +5
Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, treatment and consultation. The development of MD…
A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles
Jiahuan Pei, Pengjie Ren, Maarten de Rijke
There is increasing interest in developing personalized Task-oriented Dialogue Systems (TDSs). Previous work on personalized TDSs often assumes that complete user profiles are avai…
Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation
Jiahuan Pei, Pengjie Ren, Christof Monz +1
Dialogue response generation (DRG) is a critical component of task-oriented dialogue systems (TDSs). Its purpose is to generate proper natural language responses given some context…
A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts
Jiahuan Pei, Pengjie Ren, Maarten de Rijke
End-to-end Task-oriented Dialogue Systems (TDSs) have attracted a lot of attention for their superiority (e.g., in terms of global optimization) over pipeline modularized TDSs. Pre…
SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection
Jiahuan Pei, Arent Stienstra, Julia Kiseleva +1
Dialogue response selection is an important part of Task-oriented Dialogue Systems (TDSs); it aims to predict an appropriate response given a dialogue context. Obtaining key inform…