Papers by Zongxia Li
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence (2024.findings-emnlp)
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| Challenge: | Current short-form QA evaluations lack diverse styles of evaluation data and rely on expensive and slow LLMs. |
| Approach: | They propose a rubric for machine QA that is more stable than an exact match and neural methods. |
| Outcome: | The proposed evaluations improve on the existing short-form QA evaluations using the Trivia community. |
Large Language Models Struggle to Describe the Haystack without Human Help: A Social Science-Inspired Evaluation of Topic Models (2025.acl-long)
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Zongxia Li, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Paiheng Xu, Daniel Kofi Stephens, Juan Francisco Fung, Alden Dima, Jordan Lee Boyd-Graber
| Challenge: | a common use of NLP is to facilitate the understanding of large document collections. |
| Approach: | They propose to use large language models to replace probabilistic topic models in real-world applications. |
| Outcome: | The proposed model generates more human-readable topics and shows higher average win probabilities than traditional models for data exploration. |
SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement (2024.findings-emnlp)
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| Challenge: | Current text-to-image models struggle with generating accurate diagrams from long-context inputs. |
| Approach: | They propose a task that extracts relevant information from scientific papers and generates diagrams based on user intentions using intermediate code generation. |
| Outcome: | The proposed task outperforms existing models on factual correctness and visual appeal and outperfies existing ones on automatic and human judgement. |
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis (2024.eacl-long)
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Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber
| Challenge: | Existing evaluation metrics such as coherence and coherency are inadequate for neural topic models. |
| Approach: | They conduct the first evaluation of neural, supervised and classical topic models in an interactive task-based setting. |
| Outcome: | The proposed model performs better on cluster evaluation metrics and human evaluations than classical models on real-world tasks. |
Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender? (2024.acl-short)
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| Challenge: | We study whether large language models exhibit race- and gender-based name discrimination in hiring decisions . |
| Approach: | They propose templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. |
| Outcome: | The proposed model generates an acceptance or rejection email based on the applicant's first name . |
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators (2026.findings-acl)
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| Challenge: | State-of-the-art NLP models are expensive and inefficient for event annotation. |
| Approach: | They propose to integrate LLMs into a holistic workflow that summarizes news with event coreference resolution and argument extraction in three modes: AI-only, AI assistance, and human only. |
| Outcome: | The proposed workflow integrates LLMs to alleviate human labor in a holistic pipeline. |
SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models (2023.eacl-main)
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| Challenge: | Existing diagnostic tests for detecting social biases in NLP models only detect stereotypic associations pre-specified by the designer. |
| Approach: | They propose an approach for automatic social bias discovery in social commonsense question-answering by substituting names associated with different demographic groups and generating many distractor answers from a masked language model. |
| Outcome: | The proposed approach uncovers model’s stereotypic associations between demographic groups and an open set of words. |