2 citations · 3 across the 7 of their papers we have counts for
7 papers
MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data
Zongxia Li, Hongyang Du, Chengsong Huang +8
Self-evolving has emerged as a key paradigm for improving foundational models such as Large Language Models (LLMs) and Vision Language Models (VLMs) with minimal human intervention…
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators
Feng Gu, Zongxia Li, Carlos Rafael Colon +3
Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annotators set the gold standards, hu…
Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of Topic Models
Zongxia Li, Lorena Calvo-Bartolomé, Alexander Hoyle +4
A common use of NLP is to facilitate the understanding of large document collections, with a shift from using traditional topic models to Large Language Models. Yet the effectivene…
SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement
Ishani Mondal, Zongxia Li, Yufang Hou +3
Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and a…
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis
Zongxia Li, Andrew Mao, Daniel Stephens +5
Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as coherence are often us…
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence
Zongxia Li, Ishani Mondal, Yijun Liang +2
Question answering (QA) can only make progress if we know if an answer is correct, but current answer correctness (AC) metrics struggle with verbose, free-form answers from large l…
CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering
Zongxia Li, Ishani Mondal, Yijun Liang +2
Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current evaluation metrics to d…