5 papers
EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs
Liang Lin, Chunxi Luo, Kaiwen Luo +9
Audio Large Language Models (ALLMs) are highly vulnerable to real-world noise, which often induces severe semantic drift and hallucinations. Existing robustness methods primarily r…
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…