Papers by Zhengping Liu
Core: Robust Factual Precision with Informative Sub-Claim Identification (2025.findings-acl)
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Zhengping Jiang, Jingyu Zhang, Nathaniel Weir, Seth Ebner, Miriam Wanner, Kate Sanders, Daniel Khashabi, Anqi Liu, Benjamin Van Durme
| Challenge: | Using the Decompose-Then-Verify framework, such as FActScore, can be manipulated by adding obvious or repetitive subclaims to artificially inflate scores. |
| Approach: | They propose a decomposition-based tool called Core to filter subclaims based on their uniqueness and informativeness. |
| Outcome: | The proposed evaluation framework supports easy and modular use of Core and various decomposition strategies. |
Calibrating Zero-shot Cross-lingual (Un-)structured Predictions (2022.emnlp-main)
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| Challenge: | Existing need for model calibration when natural language models are deployed in critical tasks. |
| Approach: | They compare model calibration methods in a context of zero-shot cross-lingual transfer with pre-trained language models. |
| Outcome: | The proposed method fails to calibrate more complex confidence estimations in structured predictions compared to expressive alternatives like Gaussian Process Calibration. |
RORA: Robust Free-Text Rationale Evaluation (2024.acl-long)
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| Challenge: | Existing metrics rely on degree to which rationale supports a label, but they fail to evaluate rationales that inadvertently leak the label. |
| Approach: | They propose a RObust free-text RAtionale evaluation against label leakage that quantifies the new information supplied by a rationale to justify the label. |
| Outcome: | The proposed evaluation outperforms existing methods in evaluating human-written, synthetic, or model-generated rationales, particularly demonstrating robustness against label leakage. |
DHP Benchmark: Are LLMs Good NLG Evaluators? (2025.findings-naacl)
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Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang, Zhuoer Wang, Yingchi Liu, Mark Cusick, Param Kulkarni, Zhengping Ji, Yasser Ibrahim, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks. |
| Approach: | They propose a framework that measures the discernment of Large Language Models (LLMs) across diverse NLG tasks. |
| Outcome: | The proposed framework provides quantitative discernment scores for LLMs across four NLG tasks. |
PubMedQA: A Dataset for Biomedical Research Question Answering (D19-1)
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| Challenge: | PubMedQA is a biomedical question answering dataset based on PubMed abstracts . 68.1% accuracy is achieved, compared to single human performance of 78.0% . |
| Approach: | They propose a biomedical question answering dataset from PubMed abstracts . the dataset is annotated by experts and has 1k instances of QA . |
| Outcome: | The proposed model achieves 68.1% accuracy compared to human performance of 78.0% and majority-baseline of 55.2%. |