Papers by Zhengping Jiang
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. |
A Closer Look at Claim Decomposition (2024.starsem-1)
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| Challenge: | Recent work uses claim decomposition to determine how well supported a claim is for applications in factual precision of generated text, entailment of human generated text and claim verification. |
| Approach: | They propose an LLM-based approach to generating decompositions inspired by Bertrand Russell’s theory of logical atomism and neo-Davidsonian semantics and demonstrate its improved decomposing quality over previous methods. |
| Outcome: | The proposed method improves on the FActScore and a Bertrand Russell-inspired approach to generating decompositions inspired by neo-Davidsonian semantics and improves decomposability quality. |
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. |
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic (2024.emnlp-main)
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Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Jansen, Peter Clark, Benjamin Van Durme
| Challenge: | Recent language models allow structured reasoning with text, but lack of a clear protocol for discerning entailment causes noisy datasets and limited performance gains. |
| Approach: | They propose a consistent approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. |
| Outcome: | The proposed approach has higher internal consistency than prior decompositional entailment datasets and significantly improves proof quality and accuracy. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)
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Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme
| Challenge: | CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview. |
| Approach: | They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
| Outcome: | The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
How Grounded is Wikipedia? A Study on Structured Evidential Support and Retrieval (2026.findings-acl)
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William Gantt Walden, Kathryn Ricci, Miriam Wanner, Zhengping Jiang, Chandler May, Rongkun Zhou, Benjamin Van Durme
| Challenge: | 22% of claims in Wikipedia *lead* sections are unsupported by the article body . 30% of annotated claims in the article *body* are unbacked by their (publicly accessible) sources . |
| Approach: | They analyze Wikipedia's claim support annotations using a large-scale dataset . they find that 22% of Wikipedia claims are unsupported by the article body . |
| Outcome: | The proposed dataset analyzes claims support annotations on biographical Wikipedia articles. |
Uncertain Natural Language Inference (2020.acl-main)
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| Challenge: | Existing categorical bin assignments for NLI are not effective for entailment inference. |
| Approach: | They propose a refinement of Natural Language Inference that targets direct prediction of subjective probability assessments. |
| Outcome: | The proposed model can be used in pre-training and correlates well with humans. |