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Pengda Qin

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3 papers

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NeurIPS Conference 2025 Conference Paper

FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models

Jintao Tong
Wenwei Jin
Pengda Qin
Anqi Li
Yixiong Zou
Yuhong Li
Yuhua Li
Ruixuan Li

Large vision-language models (LVLMs) excel at multimodal understanding but suffer from high computational costs due to redundant vision tokens. Existing pruning methods typically rely on single-layer attention scores to rank and prune redundant visual tokens to solve this inefficiency. However, as the interaction between tokens and layers is complicated, this raises a basic question: Is such a simple single-layer criterion sufficient to identify redundancy? To answer this question, we rethink the emergence of redundant visual tokens from a fundamental perspective: information flow, which models the interaction between tokens and layers by capturing how information moves between tokens across layers. We find (1) the CLS token acts as an information relay, which can simplify the complicated flow analysis; (2) the redundancy emerges progressively and dynamically via layer-wise attention concentration; and (3) relying solely on attention scores from single layers can lead to contradictory redundancy identification. Based on this, we propose FlowCut, an information-flow-aware pruning framework, mitigating the insufficiency of the current criterion for identifying redundant tokens and better aligning with the model's inherent behaviors. Extensive experiments show FlowCut achieves superior results, outperforming SoTA by 1. 6% on LLaVA-1. 5-7B with 88. 9% token reduction, and by 4. 3% on LLaVA-NeXT-7B with 94. 4% reduction, delivering 3. 2$\times$ speed-up in the prefilling stage. Our code is available at https: //github. com/TungChintao/FlowCut.

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AAAI Conference 2020 Conference Paper

Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

Pengda Qin
Xin Wang
Wenhu Chen
Chunyun Zhang
Weiran Xu
William Yang Wang

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newlyadded relations. In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen. For this purpose, we leverage Generative Adversarial Networks (GANs) to establish the connection between text and knowledge graph domain: The generator learns to generate the reasonable relation embeddings merely with noisy text descriptions. Under this setting, zero-shot learning is naturally converted to a traditional supervised classiﬁcation task. Empirically, our method is model-agnostic that could be potentially applied to any version of KG embeddings, and consistently yields performance improvements on NELL and Wiki dataset.

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AAAI Conference 2020 Conference Paper

Multi-Task Self-Supervised Learning for Disfluency Detection

Shaolei Wang
Wangxiang Che
Qi Liu
Pengda Qin
Ting Liu
William Yang Wang

Most existing approaches to disﬂuency detection heavily rely on human-annotated data, which is expensive to obtain in practice. To tackle the training data bottleneck, we investigate methods for combining multiple self-supervised tasksi. e. , supervised tasks where data can be collected without manual labeling. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled news data, and propose two self-supervised pre-training tasks: (i) tagging task to detect the added noisy words. (ii) sentence classiﬁcation to distinguish original sentences from grammatically-incorrect sentences. We then combine these two tasks to jointly train a network. The pre-trained network is then ﬁne-tuned using human-annotated disﬂuency detection training data. Experimental results on the commonly used English Switchboard test set show that our approach can achieve competitive performance compared to the previous systems (trained using the full dataset) by using less than 1% (1000 sentences) of the training data. Our method trained on the full dataset signiﬁcantly outperforms previous methods, reducing the error by 21% on English Switchboard.

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