Papers by Mengjia Wu
HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | graph neural networks capture structured graph information, but lack integration at the reasoning level. |
| Approach: | They propose a framework that leverages graph structural information to reason interpretable academic QA results. |
| Outcome: | The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets. |
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for hallucination detection depend on internal signals like uncertainty and self-consistency checks to identify unreliable outputs. |
| Approach: | They propose a retrieval-augmented generation method to enhance hallucination detection by addressing information updating challenges. |
| Outcome: | The proposed method improves on existing methods with strong generalization capabilities. |
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation (2026.acl-long)
Copied to clipboard
| Challenge: | Legal consultation question answering presents unique challenges compared to traditional legal QA tasks . |
| Approach: | They propose a framework that converts queries into a legal element graph . jurisMA supports dynamic routing, statutory grounding, and stylistic optimization . |
| Outcome: | The proposed framework outperforms general-purpose and legal-domain LLMs across multiple lexical and semantic metrics. |
Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness . |
| Approach: | They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations . |
| Outcome: | The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples . |