6 papers
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning
Jingxiang Chen, Minseok Kim, Seong-Gyun Leem +13
Speech large language models (LLMs) observe paralinguistic cues such as prosody, emotion, and non-verbal sounds--crucial for intent understanding. However, leveraging these cues fa…
WearVox: An Egocentric Multichannel Voice Assistant Benchmark for Wearables
Zhaojiang Lin, Yong Xu, Kai Sun +17
Wearable devices such as AI glasses are transforming voice assistants into always-available, hands-free collaborators that integrate seamlessly with daily life, but they also intro…
Training LLMs Beyond Next Token Prediction -- Filling the Mutual Information Gap
Chun-Hao Yang, Bo-Han Feng, Tzu-Yuan Lai +3
Optimizing training performance in large language models (LLMs) remains an essential challenge, particularly in improving model performance while maintaining computational costs. T…
ConfRAG: Confidence-Guided Retrieval-Augmenting Generation
Yin Huang, Yifan Ethan Xu, Kai Sun +12
Can Large Language Models (LLMs) be trained to avoid hallucinating factual statements, and can Retrieval-Augmented Generation (RAG) be triggered only when necessary to reduce retri…
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?
Kai Sun, Yin Huang, Srishti Mehra +11
The advent of Large Language Models (LLMs) has significantly advanced web-based Question Answering (QA) systems over semi-structured content, raising questions about the continued…
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning
Mohammad Kachuee, Teja Gollapudi, Minseok Kim +10
Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual underst…