Papers by Fengbin Zhu
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs (2024.lrec-main)
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| Challenge: | Existing work on document visual question answering fails to capture the differences and correlations between elements of a document and associated questions. |
| Approach: | They propose a document-visual question-answering challenge that exploits element-level semantics and employs hierarchical Graph structures to capture differences and correlations between elements. |
| Outcome: | The proposed model surpasses the state-of-the-art method and large language model in terms of Exact Match (EM) metric, demonstrating exceptional effectiveness. |
Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs. |
| Approach: | They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts. |
| Outcome: | The proposed framework outperforms baseline methods on personalized review generation. |
PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)
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| Challenge: | Existing reward models evaluate empathy from a single perspective, overlooking bidirectional interaction nature of empathy. |
| Approach: | They propose a reward model that evaluates empathy from a single perspective . they propose PERM to integrate a bystander perspective to monitor overall interaction quality . |
| Outcome: | a new reward model outperforms state-of-the-art models on an emotional intelligence benchmark and an industrial daily conversation dataset. |
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022.acl-long)
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| Challenge: | Existing NDR models suffer from large performance drop on hypothetical questions, e.g., “what the annualized rate of return would be if the revenue in 2020 was doubled”. |
| Approach: | They propose a learning to imagine module which can be seamlessly incorporated into NDR models to perform the imagination of unseen counterfactual. |
| Outcome: | The proposed model can perform the imagination of unseen counterfactuals on hypothetical questions. |
Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection (2024.findings-emnlp)
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| Challenge: | Existing approaches to self-detection only retrospectively evaluate LLM-generated answers, leading to over-trust in incorrectly generated answers. |
| Approach: | They propose a self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers to mitigate the over-trust in LLM generated incorrect answers. |
| Outcome: | The proposed framework can be integrated with existing approaches for superior self-detection. |
TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards (2026.acl-long)
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| Challenge: | Existing approaches to training multi-turn attackers to probe model safety vulnerabilities rely on turn-level optimization, which is insufficient for learning long-term attack strategies. |
| Approach: | They propose a multi-turn reinforcement learning problem that optimizes the harmfulness of the final-turn response as the outcome reward. |
| Outcome: | The proposed approach improves attack success rates across multiple models and benchmarks, highlighting the effectiveness of the proposed approach. |