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Ming-Syan Chen

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

DeFuzzRAG: Handling Fuzzy Time Expressions for Temporal Robustness in Retrieval-Augmented Generation

Ling-Chun Chen
Hsi-Wen Chen
Ming-Syan Chen

Large Language Models (LLMs) have achieved remarkable success across reasoning and knowledge-intensive tasks, yet their static pretraining leaves them unable to handle rapidly evolving or domain-specific knowledge. Retrieval-Augmented Generation (RAG) addresses this by grounding LLM outputs in dynamically retrieved evidence, improving factual accuracy and reducing hallucinations. However, standard RAG pipelines struggle with temporally sensitive queries, especially when documents contain fuzzy or indirect time expressions (e.g., “a few years later”). This leads to Temporal Misalignment, where topically relevant but temporally incorrect results are retrieved. To overcome this, we propose DeFuzzRAG, a lightweight framework that enhances temporal robustness in RAG. DeFuzzRAG employs a small local language model to infer concrete time scopes from vague expressions and applies metadata-based filtering to realign retrieval with the query’s temporal intent. Experiments on a benchmark of fuzzified queries demonstrate that DeFuzzRAG substantially improves retrieval accuracy, raising Hit Rate by 15.7% while maintaining efficiency and model-agnostic integration. Our findings highlight the importance of temporal reasoning in RAG and establish DeFuzzRAG as a practical, plug-and-play solution for deploying temporally robust LLM systems in real-world settings.

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AAAI Conference 2026 Conference Paper

LoGIC: Multi-LoRA Guided Importance Consensus for Multi-Task Pruning in Vision Transformers

Yu-Hong Chou
Rui Fang
Hsi-Wen Chen
Ming-Syan Chen

Deploying Vision Transformers (ViTs) in real-world multi-task learning remains challenging due to their massive computational costs and the difficulty of pruning shared backbones without harming task performance. Single-task pruning often causes destructive interference by discarding weights critical to other tasks, while existing multi-task pruning strategies remain costly and unscalable for billion-parameter models. We propose Multi-LoRA Guided Importance Consensus (LoGIC), a unified framework for efficient and robust multi-task ViT pruning. LoGIC follows a two-phase procedure: (i) task-consistent pruning of LoRA modules, guided by a task-adaptive gating mechanism that balances shared and task-specific contributions while enforcing structured sparsity for deployment; and (ii) cross-task consensus pruning of the frozen ViT backbone, which retains both universally shared and task-specialized capabilities, enabling aggressive sparsity without sacrificing accuracy. Across five diverse vision benchmarks, LoGIC achieves up to 50% structured sparsity while maintaining competitive accuracy and surpassing all baselines.

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NeurIPS Conference 2025 Conference Paper

Dual Alignment Framework for Few-shot Learning with Inter-Set and Intra-Set Shifts

Siyang Jiang
Rui Fang
Hsi-Wen Chen
Wei Ding
Guoliang Xing
Ming-Syan Chen

Few-shot learning (FSL) aims to classify unseen examples (query set) into labeled data (support set) through low-dimensional embeddings. However, the diversity and unpredictability of environments and capture devices make FSL more challenging in real-world applications. In this paper, we propose Dual Support Query Shift (DSQS), a novel challenge in FSL that integrates two key issues: inter-set shifts (between support and query sets) and intra-set shifts (within each set), which significantly hinder model performance. To tackle these challenges, we introduce a Dual Alignment framework (DUAL), whose core insight is that clean features can improve optimal transportation (OT) alignment. Firstly, DUAL leverages a robust embedding function enhanced by a repairer network trained with perturbed and adversarially generated “hard” examples to obtain clean features. Additionally, it incorporates a two-stage OT approach with a negative entropy regularizer, which aligns support set instances, minimizes intra-class distances, and uses query data as anchor nodes to achieve effective distribution alignment. We provide a theoretical bound of DUAL and experimental results on three image datasets, compared against 10 state-of-the-art baselines, showing that DUAL achieves a remarkable average performance improvement of 25. 66%. Our code is available at https: //github. com/siyang-jiang/DUAL.

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ECAI Conference 2024 Conference Paper

BiLEE: Bi-Level Early Exiting for Generative Document Retrieval

Rui Fang 0002
Chin-Yuan Yeh
Hsi-Wen Chen
Ming-Syan Chen

Generative document retrieval (GDR) uses pre-trained Transformer-based large language models (LLMs) to extract contextual information and directly predict document identifier token sequences, outperforming traditional document retrieval methods. However, LLMs incur significant computational costs, hindering GDR’s practical application and making inference acceleration essential. Early exiting is one of the conditional computing techniques that expedites LLM inference, but it faces challenges when integrated into GDR due to GDR’s semantically hierarchical structured identifiers, which cause error amplification from premature exits. Moreover, although beam search expands the search space, the hierarchical structure of document identifiers restricts the diversity of initial tokens, leading to inefficiencies. In this work, we introduce Bi-Level Early Exiting for Generative Document Retrieval (BiLEE), comprising Layer Level Early Exiting (LLEE) and Token Level Early Exiting (TLEE). LLEEare designed for hierarchical document identifiers, dynamically escaping from the middle layer of the Transformer calculation based on a data-driven calibrated token threshold. TLEE exiting from unpromising candidate sequences, thus discarding unpromising search beams and enhancing beam search efficiency. Both components dynamically balance the speed-to-accuracy trade-offs for different token positions, doubling GDR’s inference speed and obtaining 13× reduction for FLOPs while maintaining the same level of accuracy. Source code: https: //github. com/Rui-Fang/BiLEE.

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AAAI Conference 2024 Conference Paper

FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting

Shu-Ling Cheng
Chin-Yuan Yeh
Ting-An Chen
Eliana Pastor
Ming-Syan Chen

To achieve better performance and greater fairness in Federated Learning (FL), much of the existing research has centered on individual clients, using domain adaptation techniques and redesigned aggregation schemes to counteract client data heterogeneity. However, an overlooked scenario exists where clients belong to distinctive groups, or, client types, in which groups of clients share similar characteristics such as device specifications or data patterns. Despite being common in group collaborations, this scenario has been overlooked in previous research, potentially leading to performance degradation and systemic biases against certain client types. To bridge this gap, we introduce Federated learning with Group Customization and Reweighting (FedGCR). FedGCR enhances both performance and fairness for FL with Distinct Client Types, consisting of a Federated Group Customization (FedGC) model to provide customization via a novel prompt tuning technique to mitigate the data disparity across different client-types, and a Federated Group Reweighting (FedGR) aggregation scheme to ensure uniform and unbiased performances between clients and between client types by a novel reweighting approach. Extensive experiment comparisons with prior FL methods in domain adaptation and fairness demonstrate the superiority of FedGCR in all metrics, including the overall accuracy and performance uniformity in both the group and the individual level. FedGCR achieves 82.74% accuracy and 12.26(↓) in performance uniformity on the Digit-Five dataset and 81.88% and 14.88%(↓) on DomainNet with a domain imbalance factor of 10, which significantly outperforms the state-of-the-art. Code is available at https://github.com/celinezheng/fedgcr.

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AAAI Conference 2023 Conference Paper

Incremental Reinforcement Learning with Dual-Adaptive ε-Greedy Exploration

Wei Ding
Siyang Jiang
Hsi-Wen Chen
Ming-Syan Chen

Reinforcement learning (RL) has achieved impressive performance in various domains. However, most RL frameworks oversimplify the problem by assuming a fixed-yet-known environment and often have difficulty being generalized to real-world scenarios. In this paper, we address a new challenge with a more realistic setting, Incremental Reinforcement Learning, where the search space of the Markov Decision Process continually expands. While previous methods usually suffer from the lack of efficiency in exploring the unseen transitions, especially with increasing search space, we present a new exploration framework named Dual-Adaptive ϵ-greedy Exploration (DAE) to address the challenge of Incremental RL. Specifically, DAE employs a Meta Policy and an Explorer to avoid redundant computation on those sufficiently learned samples. Furthermore, we release a testbed based on a synthetic environment and the Atari benchmark to validate the effectiveness of any exploration algorithms under Incremental RL. Experimental results demonstrate that the proposed framework can efficiently learn the unseen transitions in new environments, leading to notable performance improvement, i.e., an average of more than 80%, over eight baselines examined.

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AAAI Conference 2023 Conference Paper

Random Walk Conformer: Learning Graph Representation from Long and Short Range

Pei-Kai Yeh
Hsi-Wen Chen
Ming-Syan Chen

While graph neural networks (GNNs) have achieved notable success in various graph mining tasks, conventional GNNs only model the pairwise correlation in 1-hop neighbors without considering the long-term relations and the high-order patterns, thus limiting their performances. Recently, several works have addressed these issues by exploring the motif, i.e., frequent subgraphs. However, these methods usually require an unacceptable computational time to enumerate all possible combinations of motifs. In this paper, we introduce a new GNN framework, namely Random Walk Conformer (RWC), to exploit global correlations and local patterns based on the random walk, which is a promising method to discover the graph structure. Besides, we propose random walk encoding to help RWC capture topological information, which is proven more expressive than conventional spatial encoding. Extensive experiment results manifest that RWC achieves state-of-the-art performance on graph classification and regression tasks. The source code of RWC is available at https://github.com/b05901024/RandomWalkConformer.

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NeurIPS Conference 2023 Conference Paper

SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning

Yi-Chung Chen
Hsi-Wen Chen
Shun-Gui Wang
Ming-Syan Chen

The evaluation of participant contribution in federated learning (FL) has recently gained significant attention due to its applicability in various domains, such as incentive mechanisms, robustness enhancement, and client selection. Previous approaches have predominantly relied on the widely adopted Shapley value for participant evaluation. However, the computation of the Shapley value is expensive, despite using techniques like gradient-based model reconstruction and truncating unnecessary evaluations. Therefore, we present an efficient approach called Single-round Participants Amalgamation for Contribution Evaluation (SPACE). SPACE incorporates two novel components, namely Federated Knowledge Amalgamation and Prototype-based Model Evaluation to reduce the evaluation effort by eliminating the dependence on the size of the validation set and enabling participant evaluation within a single communication round. Experimental results demonstrate that SPACE outperforms state-of-the-art methods in terms of both running time and Pearson’s Correlation Coefficient (PCC). Furthermore, extensive experiments conducted on applications, client reweighting, and client selection highlight the effectiveness of SPACE. The code is available at https: //github. com/culiver/SPACE.

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NeurIPS Conference 2022 Conference Paper

ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences

Ting-An Chen
De-Nian Yang
Ming-Syan Chen

Quantization compresses models to low bits for efficient inferences which has received increasing attentions. However, existing approaches focused on balanced datasets, while imbalanced data is pervasive in the real world. Therefore, in this study, we investigate the realistic problem, quantization on class-imbalanced data. We observe from the analytical results that quantizing imbalanced data tends to obtain a large error due to the differences between separate class distributions, which leads to a significant accuracy loss. To address this issue, we propose a novel quantization framework, Class Imbalanced Quantization (ClimbQ) that focuses on diminishing the inter-class heterogeneity for quantization error reduction. ClimbQ first scales the variance of each class distribution and then projects data through the new distributions to the same space for quantization. To guarantee the homogeneity of class variances after the ClimbQ process, we examine the quantized features and derive that the homogeneity satisfies when data size for each class is restricted (bounded). Accordingly, we design a Homogeneous Variance Loss (HomoVar Loss) which reweights the data losses of each class based on the bounded data sizes to satisfy the homogeneity of class variances. Extensive experiments on class-imbalanced and benchmark balanced datasets reveal that ClimbQ outperforms the state-of-the-art quantization techniques, especially on highly imbalanced data.

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AAAI Conference 2021 Conference Paper

Hierarchical Negative Binomial Factorization for Recommender Systems on Implicit Feedback

Li-Yen Kuo
Ming-Syan Chen

When exposed to an item in a recommender system, a user may consume it (known as success exposure) or neglect it (known as failure exposure). The recently proposed methods that consider both success and failure exposure merely regard failure exposure as a constant prior, thus being capable of neither modeling various user behavior nor adapting to overdispersed data. In this paper, we propose a novel model, hierarchical negative binomial factorization, which models data dispersion via a hierarchical Bayesian structure, thus alleviating the effect of data overdispersion to help with performance gain for recommendation. Moreover, we factorize the dispersion of zero entries approximately into two lowrank matrices, thus reducing the updating time linear to the number of nonzero entries. The experiment shows that the proposed model outperforms state-of-the-art Poisson-based methods merely with a slight loss of inference speed.

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