7 citations · 32 across the 14 of their papers we have counts for
14 papers
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding
Kung-Hsiang Huang, Can Qin, Haoyi Qiu +4
Vision Language Models (VLMs) have achieved remarkable progress in multimodal tasks, yet they often struggle with visual arithmetic, seemingly simple capabilities like object count…
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
Kung-Hsiang Huang, Hou Pong Chan, Yi R. Fung +5
Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Automatic chart understanding has…
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)…
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan +5
Recent advancements in large vision-language models (LVLMs) have led to significant progress in generating natural language descriptions for visual content and thus enhancing vario…
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles
Kung-Hsiang Huang, Philippe Laban, Alexander R. Fabbri +4
Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, the summarization of diverse inform…
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…
ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media
Kung-Hsiang Huang, Hou Pong Chan, Kathleen McKeown +1
Considerable advancements have been made to tackle the misrepresentation of information derived from reference articles in the domains of fact-checking and faithful summarization.…
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…
Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation
Kung-Hsiang Huang, Kathleen McKeown, Preslav Nakov +2
Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What lim…
Document-level Entity-based Extraction as Template Generation
Kung-Hsiang Huang, Sam Tang, Nanyun Peng
Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition fr…
EventPlus: A Temporal Event Understanding Pipeline
Mingyu Derek Ma, Jiao Sun, Mu Yang +5
We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, ev…
Biomedical Event Extraction with Hierarchical Knowledge Graphs
Kung-Hsiang Huang, Mu Yang, Nanyun Peng
Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured event…
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies
Kung-Hsiang Huang, Nanyun Peng
Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a c…