Papers with AD
Thesis proposal: COGNILENS: Analyzing Cognitive Decline in Language Models for Alzheimer’s Monitoring (2026.eacl-srw)
Copied to clipboard
| Challenge: | Existing methods to detect AD and Mild Cognitive Impairment (MCI) are not effective in early stages. |
| Approach: | They propose to develop digital twins of Alzheimer's Disease using language models to mimic functional deficits observed in AD patients. |
| Outcome: | The proposed models will mimic the functional deficits observed in AD patients and evaluate their effects on brain score against the state-of-the-art models. |
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. |
DE-CLIP: Few-Shot Anomaly Detection via Difference-Guided Embedding Editing (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to detect anomalies are limited due to the lack of anomalous samples . |
| Approach: | They propose a framework that edits text embeddings based on the differences between normal and anomalous samples. |
| Outcome: | The proposed framework achieves 96.6% and 96.99% AUROC on MVTec datasets. |
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning large numbers of model parameters have shown impressive performance on the task of discriminating between language produced by cognitively healthy individuals and those with Alzheimer’s disease (AD). |
| Approach: | They propose to use a Transformer DL model pre-trained on general English text to combine an artificially degraded version of itself with a model that generalizes well to spontaneous conversations. |
| Outcome: | The proposed method generalizes well to spontaneous conversations and generates text with characteristics associated with AD, demonstrating the induction of dementia-related linguistic anomalies. |
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 . |
Can LLMs Find a Needle in a Haystack? A Look at Anomaly Detection Language Modeling (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Anomaly detection (AD) is a problem in machine learning, but it is not always competitive on certain datasets. |
| Approach: | They propose a new approach to Anomaly detection based on large pre-trained language models in three modalities. |
| Outcome: | The proposed model beats baselines on anomaly detection when presented as imbalanced classification problem regardless of the concentration of anomalous samples. |
LoSST-AD: A Longitudinal Corpus for Tracking Alzheimer’s Disease Related Changes in Spontaneous Speech (2024.lrec-main)
Copied to clipboard
| Challenge: | Language-based biomarkers have shown promising results in differentiating those with Alzheimer’s disease (AD) diagnosis from healthy individuals, but the earliest changes in language are thought to start years or even decades before the diagnosis. |
| Approach: | They propose to use transcripts of public interviews with 20 famous figures to track language change over several decades to validate their corpus. |
| Outcome: | The proposed corpus can provide a valuable starting point for the development of early detection tools and enhance our understanding of how AD affects language over time. |
MMAD:Multi-modal Movie Audio Description (2024.lrec-main)
Copied to clipboard
| Challenge: | Current methods of creating accessible movies rely on manual work, resulting in high costs and limited scalability. |
| Approach: | They propose a multi-modal movie audio description pipeline that generates narrations of information that is not accessible through unimodal hearing in movies. |
| Outcome: | The proposed pipeline surpasses existing baselines in performance on widely used datasets. |