Papers by Aimin Yang
Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)
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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou
| Challenge: | Existing research has demonstrated that the ability of large language models (LLMs) to generate humorous sentences is limited to producing 25 unique jokes. |
| Approach: | They propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences by a Chinese Pun dataset. |
| Outcome: | The proposed method significantly outperforms baseline models on Chinese and English benchmark datasets. |
The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights (2025.acl-long)
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Yufang Liu, Yao Du, Tao Ji, Jianing Wang, Yang Liu, Yuanbin Wu, Aimin Zhou, Mengdi Zhang, Xunliang Cai
| Challenge: | Existing models that leverage visual information do not improve math reasoning performance . authors suggest that visual information is important for multimodal reasoning . |
| Approach: | They propose a dataset to require image reliance for problem-solving and challenge models with similar, yet distinct, images that change the correct answer. |
| Outcome: | The proposed model performance is unaffected by changes to or removal of images in the dataset. |
SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation (2026.findings-acl)
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| Challenge: | Text-to-Video (T2V) generation is a challenge under complex scenarios. |
| Approach: | They propose a scenario-aware and self-correcting multi-agent prompt refinement framework for T2V prompting. |
| Outcome: | The proposed framework improves text-to-video alignment and overall generation quality under complex scenarios. |
An Effective Deployment of Contrastive Learning in Multi-label Text Classification (2023.findings-acl)
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| Challenge: | Existing studies on contrastive learning in natural language processing tasks have not explored the effectiveness of the technology. |
| Approach: | They propose five novel contrastive losses for multi-label text classification tasks that exploit the complexity of the input logic and the semantic representation space. |
| Outcome: | The proposed contrastive losses improve multi-label text classification tasks and can be adapted for multi-task tasks. |
Jailbreaking? One Step Is Enough! (2025.acl-long)
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Weixiong Zheng, Peijian Zeng, YiWei Li, Hongyan Wu, Nankai Lin, Junhao Chen, Aimin Yang, Yongmei Zhou
| Challenge: | Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. |
| Approach: | They propose a Reverse Embedded Defense Attack mechanism that disguises the attack intention as the "defense" intention against harmful content. |
| Outcome: | The proposed method outperforms existing methods on open-source and closed-source models and enables successful jailbreak in one iteration. |
IndoCL: Benchmarking Indonesian Language Development Assessment (2024.findings-emnlp)
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| Challenge: | Recent interest has surged in applying natural language processing (NLP) and machine learning (ML) to evaluate language development in both first (L1) and second (L2) language acquisition. |
| Approach: | They propose to use an Indonesian corpus as a benchmark for LDA tasks and to use existing large-scale language models to improve performance. |
| Outcome: | The proposed model extracts language-independent features, relieving laborious computation and reliance on specific language. |
Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution (2026.findings-acl)
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| Challenge: | Existing dynamic schemes such as early-exit and layer-drop reduce FLOPs but break batch processing or introduce KV-cache inconsistency. |
| Approach: | They propose a dynamic low-rank substitution framework that employs a lightweight decision module at each layer to dynamically determine the execution branch for different tokens. |
| Outcome: | The proposed model reduces computation by approximately 40% compared to the original dense model while outperforming existing baseline methods. |
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)
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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, but their proficiency and reliability in the specialized domain of financial data analysis remain uncertain. |
| Approach: | FinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models (LLMs) it comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. |
| Outcome: | FinDABench measures the financial data analysis capabilities of large language models (LLMs) across three dimensions: 1) Core Ability; 2) Analytical Ability; 3) Technical Ability. |
FlattenQuant: Breaking through the Inference Compute-bound for Large Language Models with Per-tensor Quantization (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have demonstrated state-of-the-art accuracies across tasks, but their latency and GPU memory consumption limit their performance. |
| Approach: | They propose a method which flattens the tensor to achieve low bit per-tensori quantization with minimal accuracy loss. |
| Outcome: | The proposed method achieves low bit per-tensor quantization with minimal accuracy loss. |
Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models (2025.coling-main)
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| Challenge: | Existing methods to augment vocabularies ignore the disparities between model representation and frequency distributions. |
| Approach: | They propose an Entropy-Consistency Word Selection method which integrates semantic and frequency metrics for vocabulary augmentation. |
| Outcome: | The proposed method improves performance for low-resource languages compared to high-resourced ones . it integrates semantic and frequency metrics for vocabulary augmentation . |
Pseudo-label Data Construction Method and Syntax-enhanced Model for Chinese Semantic Error Recognition (2025.coling-main)
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| Challenge: | Existing research on Chinese text error recognition has focused on pre-trained models, but training them from scratch is time-consuming and laborious. |
| Approach: | They propose a method for Chinese Semantic Error Recognition that generates pseudo-labels for augmented samples based on perplexity and model respectively. |
| Outcome: | The proposed method surpasses existing models in Chinese text error recognition due to Chinese semantics' complexity. |