Papers by Qingyi Wang

FaithLens: Detecting and Explaining Faithfulness Hallucination (2026.findings-acl)

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Shuzheng Si, Qingyi Wang, Haozhe Zhao, Yuzhuo Bai, Guanqiao Chen, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun

Responsible & Trustworthy NLP

Challenge:	Recent progress in large language models (LLMs) has revolutionized text generation.
Approach:	They propose a faithfulness hallucination detection model that can provide binary predictions and corresponding explanations to improve trustworthiness.
Outcome:	The proposed model outperforms advanced models on 12 diverse tasks.

Multimodal Table Understanding (2024.acl-long)

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Mingyu Zheng, Xinwei Feng, Qingyi Si, Qiaoqiao She, Zheng Lin, Wenbin Jiang, Weiping Wang

Multimodality

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.

An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)

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Qingyi Si, Tong Wang, Zheng Lin, Xu Zhang, Yanan Cao, Weiping Wang

Language Models

Challenge:	emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage.
Approach:	They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions.
Outcome:	The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions.

Towards One-to-Many Visual Question Answering (2024.findings-emnlp)

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Huishan Ji, Qingyi Si, Zheng Lin, Yanan Cao, Weiping Wang

Multimodality

Challenge:	Existing Visual Question Answering systems are constrained to support domain-specific questions . a model trained on a single specific domain may not be competent for real-world application.
Approach:	They propose a task to enable a single model to answer as many different domains of questions as possible . they break the task down into the integration of three key abilities .
Outcome:	The proposed model can answer as many domains of questions as possible, the authors argue . the proposed model generalizes well to three extra zero-shot datasets, and the results are published.

AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding (2025.findings-acl)

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Xiao Wang, Qingyi Si, Shiyu Zhu, Jianlong Wu, Li Cao, Liqiang Nie

Multimodality

Challenge:	Multimodal Large Language Models (MLLMs) are limited by context length when processing long videos.
Approach:	They propose a training-free method that flexibly reduces redundancy by allocating compression ratios among time and model layers with theoretical guarantees.
Outcome:	Experiments on videoMME, MLVU, LongVideoBench, and LVBench show that AdaRETAKE outperforms existing methods by 2.3% and 2.8% for 7B and 72B models.

Autoregressive Pre-Training on Pixels and Texts (2024.emnlp-main)

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Yekun Chai, Qingyi Liu, Jingwu Xiao, Shuohuan Wang, Yu Sun, Hua Wu

Language Models

Challenge:	pixel-based language modeling integrates visual and textual data to improve performance of language models.
Approach:	They propose a method that integrates visual and textual data into an autoregressive framework.
Outcome:	The proposed method improves performance of pixel-based language models by incorporating visual and textual data.

Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning (2022.findings-emnlp)

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Qingyi Si, Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou

Semantic Text Processing

Challenge:	Recent studies have shown that biased samples can be brittle for VQA models . however, the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data.
Approach:	They propose a contrastive learning approach that exploits biased samples for unbiased information that contributes to reasoning.
Outcome:	The proposed method achieves competitive performance on the OOD dataset while maintaining robustness on the ID dataset.

On Training Data Influence of GPT Models (2024.emnlp-main)

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Yekun Chai, Qingyi Liu, Shuohuan Wang, Yu Sun, Qiwei Peng, Hua Wu

Language Models

Challenge:	generative language models have redefined performance standards across tasks . current research on the influence of training data on autoregressivity remains underexplored .
Approach:	They propose a parameterized simulation to assess the impact of training examples on the training dynamics of GPT models.
Outcome:	The proposed approach compares existing methods with existing methods across training scenarios in generative language models, spanning tasks across 14 million to 2.8 billion parameters.

Check It Again:Progressive Visual Question Answering via Visual Entailment (2021.acl-long)

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Qingyi Si, Zheng Lin, Ming yu Zheng, Peng Fu, Weiping Wang

Visual Data in NLP

Challenge:	Existing approaches to Visual Question Answering (VQA) only address superficial correlations between image and answer.
Approach:	They propose a select-and-rerank progressive framework based on Visual Entailment to address this problem.
Outcome:	The proposed framework improves on the Visual Question Answering (VQA) task with 7.55% accuracy.

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

Question Answering

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.

Combo of Thinking and Observing for Outside-Knowledge VQA (2023.acl-long)

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Qingyi Si, Yuchen Mo, Zheng Lin, Huishan Ji, Weiping Wang

Multimodality

Challenge:	Existing approaches to visual question answering use external knowledge to acquire and use knowledge beyond images.
Approach:	They propose to constrain the cross-modality space into the same space of natural-language space . they propose a multimodal encoder, textual encoder and answer decoder to introduce more types of knowledge .
Outcome:	The proposed framework outperforms the state-of-the-art by 6.17% accuracy on a cross-modal space and natural-language space.

Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering (2023.emnlp-main)

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Qingyi Si, Yuanxin Liu, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang

Robustness in NLP

Challenge:	Existing studies on VQA models have found that they suffer from dataset biases and inefficient memory footprints.
Approach:	They investigate whether a VLP can be compressed and debiased simultaneously by searching sparse and robust subnetworks.
Outcome:	The proposed compression and debiasing pipelines outperform the debiased full VLPs on VQA tasks.