7 citations · 11 across the 4 of their papers we have counts for
4 papers
Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate
Kyungha Kim, Sangyun Lee, Kung-Hsiang Huang +3
Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust. While Large Language Models (LLMs)…
Zero-shot Faithful Factual Error Correction
Kung-Hsiang Huang, Hou Pong Chan, Heng Ji
Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models. Drawing on h…
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma +5
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries. To address this issue,…
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval
Kung-Hsiang Huang, ChengXiang Zhai, Heng Ji
Fact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data sca…