Papers by Minhao Zhang
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA (2025.findings-acl)
Copied to clipboard
Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li
| Challenge: | Current long-context large language models lack citations to support their responses, making verification difficult due to potential hallucinations. |
| Approach: | They propose to use off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations and leverage this pipeline to construct a large-scale SFT dataset for LQAC. |
| Outcome: | The proposed pipeline can generate responses with fine-grained citations on the fly, surpassing existing models including GPT-4o. |
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering (2021.naacl-demos)
Copied to clipboard
| Challenge: | Using a node-based framework, knowledge base question answering systems can grasp structural mappings between questions and KB queries. |
| Approach: | They propose a node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. |
| Outcome: | The proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. |
Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline . |
| Approach: | They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs. |
| Outcome: | Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient. |
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning (2026.acl-long)
Copied to clipboard
Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang
| Challenge: | Multimodal mathematical Reasoning (MMR) has attracted increasing attention for its ability to solve mathematical problems involving both textual and visual modalities. |
| Approach: | They review the theoretical frameworks of multimodal reasoning and examine the challenges they face in visual math tasks. |
| Outcome: | The proposed models can solve problems involving both textual and visual modalities. |
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP (2025.acl-long)
Copied to clipboard
| Challenge: | MU has gained significant attention as a means to remove the influence of specific data from a trained model without requiring full retraining. |
| Approach: | They propose a novel approach that disentangles and selectively forgets both visual and textual associations, ensuring that unlearning does not compromise model performance. |
| Outcome: | Experiments on CIFAR-100, Flickr30K, and Conceptual 12M show that CLIPErase effectively removes designated associations from multimodal samples in downstream tasks while preserving model performance on retain set. |
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for text classification use label names of target classes as the only supervision. |
| Approach: | They propose a method that uses keyword-based keyword matching to generate pseudo labels . they propose 'pieclass' module that iteratively trains classifiers and updates pseudo labels. |
| Outcome: | The proposed method achieves better performance than existing strong baselines on seven benchmark datasets and similar performance to fully-supervised classifiers on sentiment classification tasks. |
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs. |
| Approach: | They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts. |
| Outcome: | The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts. |