Papers by Qingyi Liu
Autoregressive Pre-Training on Pixels and Texts (2024.emnlp-main)
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| 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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| 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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| 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. |
Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning (2026.acl-long)
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Ziheng Li, Liu Kang, Feng Xiao, Luxi Xing, Qingyi Si, Zhuoran Li, Weikang Gong, Deqing Yang, Yanghua Xiao, Hongcheng Guo
| Challenge: | Group Relative Policy Optimization (GRPO) uses a coarse-grained credit assignment mechanism that propagates group-level rewards uniformly to to every token in a sequence, neglecting the varying contribution of individual reasoning steps. |
| Approach: | They introduce Outcome-grounded Advantage Reshaping (OAR) which redistributes advantages based on how much each token influences the model’s final answer. |
| Outcome: | Empirical results show that OAR-G outperforms GRPO on a high-fidelity attribution signal and suppresses low-impact tokens while preserving the advantage mass. |
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. |
Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering (2023.emnlp-main)
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| 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. |