110 citations · 322 across the 16 of their papers we have counts for
17 papers
Learning from Self Critique and Refinement for Faithful LLM Summarization
Ting-Yao Hu, Hema Swetha Koppula, Hadi Pouransari +3
Large Language Models (LLMs) often suffer from hallucinations: output content that is not grounded in the input context, when performing long-form text generation tasks such as sum…
Learning to Reason for Hallucination Span Detection
Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula +7
Large language models (LLMs) often generate hallucinations -- unsupported content that undermines reliability. While most prior works frame hallucination detection as a binary task…
COMPASS: Benchmarking Constrained Optimization in LLM Agents
Tian Qin, Felix Bai, Ting-Yao Hu +8
Human decision-making often involves constrained optimization. As LLM agents are deployed to assist with real-world tasks like travel planning, shopping, and scheduling, they must…
Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization
Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula +8
In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task. Unlike prior methods that require external knowledge,…
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula +5
While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data…
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Karren Yang, Ting-Yao Hu, Jen-Hao Rick Chang +2
Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount…