| Challenge: | In the United States, adults over 65 are expected to comprise one-fifth of the population by 2030, and a larger proportion of the . population than those under 18 by 2035. |
| Approach: | They propose a neural model that takes into account both long language samples and hand-crafted linguistic features to distinguish between dementia affected and healthy patients. |
| Outcome: | The proposed model achieves an F1 score of 0.929 on the DementiaBank dataset and the state-of-the-art on the dataset. |
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Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models (N18-2)
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| Challenge: | Current diagnoses often involve lengthy medical evaluations. |
| Approach: | They apply neural models based on CNNs, LSTM-RNNs, and their combination to classify AD and control language samples. |
| Outcome: | The proposed model achieves independent benchmark accuracy for the AD classification task. |
Adversarial Text Generation using Large Language Models for Dementia Detection (2024.emnlp-main)
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| Challenge: | Large language models excel in text classification tasks, but they do not perform well with picture description. |
| Approach: | They propose an interpretable classification approach by Adversarial Text Generation (ATG) that could relate dementia detection with other tasks. |
| Outcome: | The proposed approach achieves 85% accuracy, >10% improvement over the previous methods. |
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models (2022.acl-long)
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| 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. |
Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)
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Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis
| Challenge: | Existing work on speech and language models has been limited by the size of available datasets. |
| Approach: | They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls. |
| Outcome: | The proposed model improves classification performance in English and French separately. |
Transforming Brainwaves into Language: EEG Microstates Meet Text Embedding Models for Dementia Detection (2025.acl-srw)
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| Challenge: | Dementia is recognised as the seventh leading cause of mortality globally and plays a major role in increasing disability and dependence among older adults. |
| Approach: | They propose to represent electroencephalography microstates as symbolic, language-like sequences and use text embedding and time-series deep learning models for classification. |
| Outcome: | The proposed method achieves a high accuracy of 94.31% on 1001 EEG data from multiple countries and eliminates fixed configurations and costly/invasive modalities. |
Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness (2025.findings-emnlp)
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| Challenge: | a new study examines how dementia is perceived by non-experts . human perception of dementia is inconsistent and relies on a narrow set of cues compared to LLMs based on broader clinical patterns . |
| Approach: | They propose a method that uses LLMs to extract high-level, expert-guided features . human perception of dementia is inconsistent and relies on a narrow set of cues, they say . |
| Outcome: | The proposed method analyzes picture descriptions to assess whether they were produced by non-experts or by nonexperts. |
A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer’s Type (2020.acl-main)
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| Challenge: | Recent studies show that cognitive manifestations of future dementia may appear as early as 18 years prior to clinical diagnosis . lack of clear diagnosis and prognosis, possibly for an Alzheimer's type, is a major limitation of current methods for identifying dementia-specific cognitive markers. |
| Approach: | They propose to interrogate neural LMs trained on participants with and without dementia by manipulating lexical frequency. |
| Outcome: | The proposed model improves upon the current state-of-the-art for models trained on transcripts of speech produced by healthy participants and those with dementia. |
CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection (2023.findings-acl)
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| Challenge: | Existing methods for detecting AD are challenging and time-consuming due to lack of data and generalizability of the models. |
| Approach: | They propose a contrastive data augmentation method which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. |
| Outcome: | The proposed method achieves the best performance among language-based models on the benchmark ADReSS Challenge dataset. |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer’s Disease Detection (2025.acl-long)
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| Challenge: | Existing methods for in-context learning (ICL) perform poorly for AD diagnosis due to inherent complexity of task. |
| Approach: | They propose a demonstration selection strategy that leverages a delta score to assess the relative gains of each training example and a KNN-based retriever that dynamically selects optimal “representatives” for a given input. |
| Outcome: | The proposed model outperforms existing methods on two AD detection datasets and surpasses even supervised classifiers. |