Papers by Yingjie Zhu
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships. |
| Approach: | They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships. |
| Outcome: | The proposed method outperforms the baselines and can generate linguistically diverse counterfactuals without disrupting their logical relationships. |
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains. |
| Approach: | They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks. |
| Outcome: | Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information. |
Denoising Rationalization for Multi-hop Fact Verification via Multi-granular Explainer (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing rationalization methods for multi-hop fact verification lack nuanced composition in the evidence, which leads to noise rationalization. |
| Approach: | They propose a method to obtain rationale by completely removing subset of input without compromising verification accuracy. |
| Outcome: | The proposed method outperforms 12 baselines on three multi-hop fact verification datasets. |
SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling (2025.findings-acl)
Copied to clipboard
| Challenge: | Traditional sentiment analysis methods focus on static reviews, failing to capture temporal relationship between user sentiment rating and textual content. |
| Approach: | They propose a dynamic graph-based framework that addresses data sparsity in streaming reviews. |
| Outcome: | The proposed framework reduces data sparsity by categorizing users into mid-tail, long-tail and extreme scenarios and incorporating LLM enhancements within a dynamic graph-based structure. |
THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to risk prediction from EHRs handle structured diagnostic codes and unstructured narrative notes separately. |
| Approach: | They propose a Temporal-Hierarchical Causal Model with Conformal Calibration . they construct a multimodal causal graph where nodes represent clinical entities from two modalities . |
| Outcome: | The proposed model infers three clinically grounded interactions from textual propositions and ICD codes mapped to textual descriptions. |