Papers by Jingyi Sun
Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension (2022.acl-short)
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| Challenge: | Existing studies identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the holistic argument-level interactions. |
| Approach: | They propose to use machine reading comprehension to extract argument pairs from two documents . they propose to employ an AM query to identify all arguments in two documents, then an APE query to extract its paired arguments from another document. |
| Outcome: | The proposed method outperforms the state-of-the-art method by 7.11% in F1 score. |
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021.emnlp-main)
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| Challenge: | Existing studies on argumentation mining focus on monological argumentation and dialogical argumentation. |
| Approach: | They propose a mutual guidance framework that could guide arguments in one passage . they propose an inter-sentence relation graph to effectively model the inter-relations between two sentences . |
| Outcome: | The proposed method outperforms the current state-of-the-art model. |
MisinfoBench: A Multi-Dimensional Benchmark for Evaluating LLMs’ Resilience to Misinformation (2025.findings-emnlp)
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| Challenge: | Existing benchmarks assess factual accuracy in isolated queries but fail to evaluate LLMs’ resilience to misinformation in interactive settings. |
| Approach: | MisinfoBench is a benchmark designed to assess LLMs’ ability to discern, resist, and reject misinformation. |
| Outcome: | MisinfoBench assesses large language models’ ability to discern, resist, and reject misinformation in interactive settings. |
Graph-Guided Textual Explanation Generation Framework (2025.emnlp-main)
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Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber, Steffen Eger, Pepa Atanasova, Isabelle Augenstein
| Challenge: | Existing work has questioned their faithfulness, as they may not accurately reflect the model’s internal reasoning process regarding its predicted answer. |
| Approach: | They propose a Graph-Guided Textual Explanation Generation framework that generates a graph neural network layer that guides the NLE generation and generates explanations with greater semantic and lexical similarity to human-written ones. |
| Outcome: | The proposed framework improves NLE faithfulness by up to 12.12% compared to baseline methods on encoder-decoder and decoder-only models. |
Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework (2025.naacl-long)
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| Challenge: | Input feature explanations reveal how a model makes decisions based on a specific input. |
| Approach: | They propose a framework that facilitates an automated comparison between highlight and interactive explanations comprised of four diagnostic properties. |
| Outcome: | The proposed framework compares highlight and interactive explanations across two datasets and two models and shows that interactive span explanations outperform other explanation types across most diagnostic properties. |
Explaining Sources of Uncertainty in Automated Fact-Checking (2026.acl-long)
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| Challenge: | Existing methods to explain model uncertainty as numbers or hedges do not reveal which evidence conflicts cause the uncertainty, leaving users unable to resolve disagreements. |
| Approach: | They propose a plug-and-play framework that generates natural-language explanations of model uncertainty grounded in conflicting/agreeing evidence. |
| Outcome: | The proposed framework generates explanations that more faithfully track model uncertainty and better align with the model’s fact-checking decisions than span-agnostic explanation prompting. |