Papers with machine learning task
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)
Copied to clipboard
Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
| Challenge: | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. |
| Approach: | They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection. |
| Outcome: | The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models. |
NLP-ADBench: NLP Anomaly Detection Benchmark (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Anomaly detection (AD) is an important machine learning task, but its effectiveness in detecting harmful content, phishing attempts, and spam reviews is limited. |
| Approach: | They introduce NLP-ADBench, the most comprehensive NLP anomaly detection benchmark to date . it includes eight curated datasets and 19 state-of-the-art algorithms . |
| Outcome: | The NLP-ADBench benchmark includes 19 state-of-the-art methods and 8 curated datasets . no single model dominates across all datasets, indicating need for automated model selection . |
Frustratingly Easy System Combination for Grammatical Error Correction (2022.naacl-main)
Copied to clipboard
| Challenge: | Using a simple logistic regression algorithm, we combine GEC models for binary classification. |
| Approach: | They propose a logistic regression algorithm that can combine GEC models with binary classification. |
| Outcome: | The proposed method outperforms the state-of-the-art by 4.2 points on the CoNLL-2014 and 7.2 points on BEA-2019 test sets. |
Jump To Hyperspace: Comparing Euclidean and Hyperbolic Loss Functions for Hierarchical Multi-Label Text Classification (2025.coling-main)
Copied to clipboard
| Challenge: | Hierarchical Multi-Label Text Classification (HMTC) is a challenging machine learning task . a recent study evaluated the effectiveness of Euclidean and hyperbolic loss functions on HMTC . |
| Approach: | They evaluate label-aware and contrastive losses in the Euclidean and hyperbolic space . they find contrastive loss functions are less effective when deployed in the hyperbolical space compared to non-hyperbolic ones . |
| Outcome: | The proposed model improves on four commonly used HMTC datasets. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
Copied to clipboard
| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
Reconsidering Sentence-Level Sign Language Translation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Historically, sign language machine translation is framed as a sentence-level task . however, there are known intersentential dependencies that are impossible to resolve in isolation. |
| Approach: | They propose a human baseline for sign language translation that substitutes a person into the machine learning task framing instead of providing the entire document as context. |
| Outcome: | The proposed human baseline for sign language translation shows that deaf signers can only understand key parts of the clip in light of additional discourse-level context. |