Papers by Wei Ju
HEAL: Hybrid Enhancement with LLM-based Agents for Text-attributed Hypergraph Self-supervised Representation Learning (2025.findings-emnlp)
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| Challenge: | Existing approaches to enhance text-attributed hypergraph self-supervised learning are limited by label scarcity. |
| Approach: | They propose a data-centric approach that leverages large language models to enhance hypergraph self-supervised learning by integrating hyperedges into a self-representation framework. |
| Outcome: | The proposed approach generates informative nodes and hyperedges through multi-round interaction with LLM-based agents. |
A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-long)
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Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang
| Challenge: | achieving data-efficient post-training of Large Language Models is a key research question. |
| Approach: | They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective. |
| Outcome: | The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems. |
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025.emnlp-main)
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Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Guanbo Wang, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang
| Challenge: | Existing methods for fine-tuning-based compression suffer from verbose outputs, increasing computational overhead. |
| Approach: | They propose a framework to generate concise reasoning chains using Confidence Injection and Early Stopping. |
| Outcome: | The proposed framework reduces the length of the model by up to 50% while maintaining high task accuracy. |
UOR: Universal Backdoor Attacks on Pre-trained Language Models (2024.findings-acl)
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| Challenge: | Existing methods to attack pre-trained language models rely on manual selection of triggers and backdoor representations. |
| Approach: | They propose a backdoor attack method that turns manual selection into automatic optimization . they propose to use poisoned contrastive learning to learn more uniform backdoor representations . |
| Outcome: | The proposed method achieves better attack performance on text classification tasks compared to manual methods. |
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models (2024.acl-long)
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| Challenge: | Recent work has demonstrated the power of large language models in recalling knowledge and reasoning. |
| Approach: | They propose to erase shortcut neurons to mitigate the associated risks . 20% of the failures are attributed to shortcuts, they find . |
| Outcome: | The proposed approach reduces failures in multi-hop knowledge editing caused by shortcuts by 20% . |
Backdoor NLP Models via AI-Generated Text (2024.lrec-main)
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| Challenge: | Existing attacks disregard fluency and semantic fidelity of poisoned text, rendering it easily detectable. |
| Approach: | They propose to use AI-generated poisoned text to attack NLP models by establishing covert associations between trigger patterns and target labels without affecting normal accuracy. |
| Outcome: | The proposed method achieves effective attacks while maintaining fluency and semantic similarity across all scenarios. |
Embracing Large Language Models in Traffic Flow Forecasting (2025.findings-acl)
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| Challenge: | Existing methods to predict future traffic flows capture spatio-temporal dependencies, but they fail to adapt to test-time environmental changes. |
| Approach: | They propose to use large language models to help traffic flow forecasting by capturing spatio-temporal dependencies and using a large language model to select the most likely result. |
| Outcome: | The proposed method is based on large language models (LLMs) and an LLM-based selector. |
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection (2022.emnlp-main)
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| Challenge: | Existing studies focus on matching between candidate options and historical dialogues while ignoring the reasoning ability of the model. |
| Approach: | They propose an Implicit Relational Reasoning Graph Network to address these issues . they propose to implicitly extract dependencies between utterances and options . |
| Outcome: | The proposed model outperforms human models on two multi-turn dialogue reasoning benchmark datasets. |
Calibrating Inference Time Alignment with Sequence-level Risk Accumulation (2026.acl-long)
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| Challenge: | Existing approaches to decode large language models (LLMs) often over-reject benign information, limiting their generalizability in real-world scenarios where harmful and benign information coexist. |
| Approach: | They propose a framework to regulate decoding alignments for Large Language Models (LLMs) they employ a reward-guided branch decoding paradigm to incorporate safety awareness during generation. |
| Outcome: | The proposed framework achieves superior performance on four attack benchmarks and two neutral datasets. |
PED: Route-Decoupled Diagnostics for Persona Consistency in Spoken Agents (2026.findings-acl)
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| Challenge: | Existing evaluations of persona-emotion decoupling frameworks do not isolate which component caused the failure . current evaluations do little to isolate the cause of the failure, making fixes slow and ad hoc . |
| Approach: | They propose a diagnostic evaluation framework that decomposes persona expression into two observable routes. |
| Outcome: | The proposed framework decomposes persona expression into two observable routes . it can be used to perform route-comparable, reference-based analyses of separability, drift, failures and coupling . |
UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
| Challenge: | Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently. |
| Approach: | They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules. |
| Outcome: | The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored. |
CoTrust: Privacy-Preserving Collaboration Between Large and Small Language Models in Trusted Execution Environments (2026.findings-acl)
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| Challenge: | Large language models (LLMs) provide powerful text generation capabilities, but accessing sensitive user inputs raises privacy concerns. |
| Approach: | They propose a privacy-preserving collaborative inference framework that combines large language models with small language models inside TEE to preserve privacy. |
| Outcome: | Experiments show that CoTrust outperforms unconstrained LLMs on multiple question answering and summarization benchmarks while maintaining strong privacy protection. |
LEAF: Large Language Diffusion Model for Time Series Forecasting (2025.findings-emnlp)
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| Challenge: | Recent work has applied large language models (LLMs) into time series forecasting, but they lack an understanding of holistic temporal patterns with potential error accumulation. |
| Approach: | They propose a framework that marries Larg e Langu age Diffusion Model with time series forecasting (LEAF) they propose converting time series into tokens and adopting language diffusion models to capture temporal dependencies. |
| Outcome: | The proposed framework generates future predictions with a diffusion model from a holistic view. |
Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness (2025.findings-emnlp)
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| Challenge: | Multi-modal large language models have been used for processing and understanding information from diverse modalities. |
| Approach: | They propose to evaluate the audio-visual capabilities of multi-modal large language models . they focus on effectiveness, efficiency, generalizability, and robustness . |
| Outcome: | The proposed models exhibit strong zero-shot and few-shot generalization abilities . their success relies heavily on the vision modality, which impairs performance when visual input is corrupted or missing. |
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study (2024.lrec-main)
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| Challenge: | Existing studies have focused on enhancing the factualness of large language models using context knowledge. |
| Approach: | They propose to use ChatGPT to construct probing datasets that provide diverse and coherent evidence corresponding to various facts. |
| Outcome: | The proposed model can encode knowledge across different layers, and it is compared with existing models. |
E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)
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Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju
| Challenge: | Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study. |
| Approach: | They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies . |
| Outcome: | The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%. |
Semi-supervised Fine-tuning for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs require labeled data, which can be costly in real-world applications. |
| Approach: | They propose a framework that can fully exploit labeled and unlabeled data for LLM fine-tuning . they conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets . |
| Outcome: | The proposed framework can fully exploit labeled and unlabeled data for LLM alignment from a propagate-and-select manner. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
DANCE: Diversity-attended Dynamic Caching with Asymmetric Quantization for Test-time Adaptation of Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing approaches to test-time adaptation of vision-language models measure prediction entropy but these samples tend to approach prototypes with limited coverage of data distributions. |
| Approach: | They propose a new approach for test-time adaptation of vision-language models . they construct a dynamic cache to store diversity-aware test samples . |
| Outcome: | The proposed approach is more efficient than current methods on augmented visual models. |