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20242026
most citedImproving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis

2 citations · 3 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.CL2024

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…

cs.CL2024★ 2 cited

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…

cs.CL2024

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…

cs.CL2024

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…