Papers by Mingyu Zheng
TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning (2025.findings-acl)
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| Challenge: | Existing methods for table instruction tuning are limited due to limited data diversity and lack of data quality. |
| Approach: | They propose a weakness-guided data synthesis framework for table instruction tuning that explores the vast input space of table understanding tasks and then iterates through the input space. |
| Outcome: | The proposed framework boosts the average accuracy of Llama3.1-8B-instruct by 11.62% with 27K GPT-4o synthetic data and outperforms state-of-the-art data synthesis baselines which use more training data. |
MAssistant: A Personal Knowledge Assistant for MOOC Learners (D19-3)
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| Challenge: | Massive Open Online Courses (MOOCs) have experienced a rapid development since 2012 . many MOOC platforms have been launched, including Coursera1 , edX2 , and Udacity3 etc. |
| Approach: | They present a personal knowledge assistant system called MAssistant for MOOC learners . MAsistants has a large-scale concept graph built from open data . it also provides a browser extension which interacts with users during video lectures . |
| Outcome: | The proposed system helps users trace the concepts they have learned in MOOCs, and to build their own concept graphs. |
Multimodal Table Understanding (2024.acl-long)
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| Challenge: | Existing approaches to understanding tables rely on textual inputs and table images are difficult to access in real-world scenarios. |
| Approach: | They propose a multimodal table understanding problem where the model needs to generate correct responses to various table-related requests based on the given table image. |
| Outcome: | The proposed model outperforms open-source MLLMs on 23 benchmarks under held-in and held-out settings. |
Chain-of-Thought Reasoning in Tabular Language Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to extend chain-of-thought reasoning into large language models are not viable in the scenario of privatization deployment or limited resources. |
| Approach: | They propose a framework that extends chain-of-thought reasoning into tabular language models . framework coordinates two TaLMs responsible for CoT generation and answer inference . |
| Outcome: | The proposed framework outperforms the state-of-the-art ChatGPT on the TABMWP dataset by 9.55% (82.60%92.15% in accuracy) with less parameters (0.8B). |
IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures (2023.acl-long)
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| Challenge: | Existing benchmarks only evaluate model performance on tables with explicit table structures, which means headers are explicitly annotated and treated as model input during inference. |
| Approach: | They propose a new Table Question Answering (TQA) dataset with implicit and multi-type table structures that requires the model to understand tables without directly available header annotations. |
| Outcome: | The proposed framework outperforms baselines on a dataset with implicit and multi-type table structures and can handle multi-table tables including previously neglected complex tables. |
Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)
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Chenxu Yang, Ruipeng Jia, Mingyu Zheng, Naibin Gu, Zheng Lin, Siyuan Chen, Weichong Yin, Hua Wu, Weiping Wang
| Challenge: | Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity. |
| Approach: | They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states. |
| Outcome: | The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters. |
PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation (2026.acl-long)
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| Challenge: | Podcast script generation is a challenging task for large language models, but evaluation resources are limited. |
| Approach: | They propose a benchmark to evaluate podcast script generation using a multifaceted evaluation framework . PodBench is a prototype that integrates quantitative constraints with LLM-based quality assessment . |
| Outcome: | The proposed framework integrates quantitative constraints with LLM-based quality assessment. |
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA (2022.findings-emnlp)
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Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou
| Challenge: | Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. |
| Approach: | They propose a dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. |
| Outcome: | The proposed dataset considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. |