19 citations · 36 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…