5 citations · 8 across the 12 of their papers we have counts for
12 papers
EchoDistill: Robust Large Audio Language Models via Noisy-to-Clean Self-Distillation
Liang Lin, Chunxi Luo, Kaiwen Luo +12
Large Audio Language Models (LALMs) remain vulnerable to acoustic noise, which can obscure task-relevant evidence and produce unreliable responses. We propose EchoDistill, a noisy-…
Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation
Hongyu Cao, Yuxuan Wu, Yucheng Cai +2
Retrieval-augmented generation (RAG) has become a widely recognized paradigm to combine parametric memory with non-parametric memories. An RAG model consists of two serial connecti…
Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems
Yucheng Cai, Yuxuan Wu, Yi Huang +2
Large language models (LLMs) have recently been applied to dialog systems. Despite making progress, LLMs are prone to errors in knowledge-intensive scenarios. Recently, approaches…
Entriever: Energy-based Retriever for Knowledge-Grounded Dialog Systems
Yucheng Cai, Ke Li, Yi Huang +2
A retriever, which retrieves relevant knowledge pieces from a knowledge base given a context, is an important component in many natural language processing (NLP) tasks. Retrievers…
The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)
Yucheng Cai, Si Chen, Yuxuan Wu +3
Recently, increasing research interests have focused on retrieval augmented generation (RAG) to mitigate hallucination for large language models (LLMs). Following this trend, we la…
Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking
Hong Liu, Yucheng Cai, Yuan Zhou +3
Continual learning is crucial for dialog state tracking (DST) in dialog systems, since requirements from users for new functionalities are often encountered. However, most of exist…
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt
Yucheng Cai, Wentao Ma, Yuchuan Wu +4
Recent research has shown that multi-task pre-training greatly improves the model's robustness and transfer ability, which is crucial for building a high-quality dialog system. How…
Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision
Yucheng Cai, Hong Liu, Zhijian Ou +2
Most existing task-oriented dialog (TOD) systems track dialog states in terms of slots and values and use them to query a database to get relevant knowledge to generate responses.…
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems
Hong Liu, Yucheng Cai, Zhijian Ou +2
Building user simulators (USs) for reinforcement learning (RL) of task-oriented dialog systems (DSs) has gained more and more attention, which, however, still faces several fundame…
Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models
Yucheng Cai, Hong Liu, Zhijian Ou +2
Developing semi-supervised task-oriented dialog (TOD) systems by leveraging unlabeled dialog data has attracted increasing interests. For semi-supervised learning of latent state T…
Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems
Hong Liu, Yucheng Cai, Zhijian Ou +2
Recently, Transformer based pretrained language models (PLMs), such as GPT2 and T5, have been leveraged to build generative task-oriented dialog (TOD) systems. A drawback of existi…
Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems
Hong Liu, Yucheng Cai, Zhenru Lin +3
Recently, two approaches, fine-tuning large pre-trained language models and variational training, have attracted significant interests, separately, for semi-supervised end-to-end t…