Papers with processing time
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation (P18-4)
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| Challenge: | Money laundering (AML) is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets. |
| Approach: | They propose a framework that uses deep learning to augment AML monitoring and investigation . money laundering is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets . |
| Outcome: | The proposed framework reduces time and cost by 30% compared to existing methods . money laundering is the world's third largest "industry" |
Low-Resource Fast Text Classification Based on Intra-Class and Inter-Class Distance Calculation (2025.coling-main)
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| Challenge: | Existing methods based on neural networks and pre-trained models consume substantial memory for training and text-graph construction. Existing models require access to the test dataset during the training phase, which means that when encountering new text data, the existing model needs to be retrained. |
| Approach: | They propose a low-resource and fast text classification model called LFTC to address these challenges by mining regularity information within intra-class data. |
| Outcome: | The proposed model improves performance and processing time under limited computational and data resources on 9 publicly available datasets. |
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models (2025.findings-acl)
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| Challenge: | Existing pruning methods for large language models (LLMs) focus on achieving high compression rates while maintaining model performance. |
| Approach: | They propose a Univeral Model for Customized Compression (UniCuCo) which introduces a StratNet that learns to map arbitrary requests to their optimal pruning strategy. |
| Outcome: | The proposed model is 28 times faster than baselines in processing 64 requests, while maintaining comparable accuracy to baselines. |
Scalable and Interpretable Semantic Change Detection (2021.naacl-main)
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| Challenge: | Several cluster-based methods for word usage change detection are unscalable in terms of memory consumption and computation time. |
| Approach: | They propose a scalable method for word usage-change detection that uses contextual embeddings to aggregate word usages into clusters. |
| Outcome: | The proposed method offers high performance and interpretability while being unscalable. |
Efficient Long Context Language Model Retrieval with Compression (2025.acl-long)
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| Challenge: | Long Context Language Models (LCLMs) are a new paradigm for Information Retrieval . however, processing large number of passages within in-context for retrieval is computationally expensive . a proposed compression approach for LCLM retrieval maximizes retrieval performance while minimizing the length of the compressed passages. |
| Approach: | They propose a new compression approach tailored to maximize retrieval performance while minimizing the length of compressed passages. |
| Outcome: | The proposed approach maximizes retrieval performance while minimizing the length of compressed passages while reducing the in-context size by 1.91. |
HyperMixer: An MLP-based Low Cost Alternative to Transformers (2023.acl-long)
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Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, Francois Marelli, Francois Fleuret, James Henderson
| Challenge: | Existing MLP-based architectures that combine multiple features are expensive and require a lot of training data. |
| Approach: | They propose a simple MLP-based model which allows token mixing by dynamically applying hypernetworks to each feature independently. |
| Outcome: | The proposed model performs better than Transformers and lowers costs in terms of processing time, training data, and hyperparameter tuning. |