Domain Adaptation via Prompt Learning for A lzheimer’s Detection (2024)

Abstract: Spoken language presents a compelling medium for non-invasive Alzheimer’s disease (AD) screening, and prior work has examined the use of fine-tuned pretrained language models (PLMs) for this purpose. However, PLMs are often optimized on tasks that are inconsistent with AD classification. Spoken language corpora for AD detection are also small and disparate, making generalizability difficult. This paper investigates the use of domain-adaptive prompt fine-tuning for AD detection, using AD classification loss as the training objective and leveraging spoken language corpora from a variety of language tasks. Extensive experiments using voting-based combinations of different prompting paradigms show an impressive mean detection F1=0.8952 (with std=0.01 and best F1=0.9130) for the highest-performing approach when using BERT as the base PLM.

## introduction alzheimer's disease (ad) results in gradual impairment of memory, executive function, and language abilities (alzheimer's association, 2023). it is not treatable, but its effects can be slowed through medical or lifestyle interventions @xcite . spoken language assessment offers a non-invasive, inexpensive, and scalable way to automate ad screening (de la fuente @xcite , and the natural language processing community has recently launched popular challenges to accelerate progress on ad detection @xcite . many approaches have used manual features @xcite @xcite , although recently the use of pretrained language models (plms) has also grown prominent @xcite @xcite @xcite figure 1 : high-level template overview for domain adaptation via prompt-based fine-tuning, showing the domain-specific template (d) and class-specific template (c) in two sample formats. @xcite @xcite . currently, the most common pipelined architecture for ad detection feeds text embeddings produced by plms into back-end classification layers optimized for the task @xcite , but the discrepency between ad classification and the plm feature extractor's loss function remains unaddressed. furthermore, since ad detection datasets are small and involve varied language tasks, cross-domain foot_0 application of fine-tuned plms remains underexplored. we address these limitations by framing crossdomain ad detection as a supervised domain adaptation problem, and experiment with cloze-style prompt-based learning @xcite ) for this purpose. there has been limited research regarding the effectiveness of prompt-based learning for medical applications @xcite , particularly in the cognitive health domain @xcite . we introduce a novel prompt learning approach for cross-domain adaptation, domain adaptation via prompt-based fine-tuning (dapf, partially summarized in figure 1 ), that optimizes plms along with domain-adaptive prompt parameters while training the model with source and target domain data. this dynamically adapts the classifier to each domain. we show that dapf outperforms both p-tuning @xcite and prompt-based fine-tuning @xcite ) that lacks domain information. our contributions include: in contrast, prior relevant prompting research focused on a particular ad corpus @xcite or clinical tasks with different characteristics @xcite . results from our experiments show that dapf outperforms contemporary alternatives. additionally, domain-adaptive manual prompt finetuning outperforms domain-adaptive soft prompting with frozen plm weights for ad detection. ## results we present our results in tables 3 and 4 . since we experimented with different prompt lengths and positions for dapf, we report results for the three top-performing dapf models (these were also the models included in dapf ensemble). we indicate the prompt form in the template column of tables 3 and 4 . alphanumeric characters in parentheses in the prompt form specify components defined in ## conclusion in this paper we systematically and comprehensively investigated the use of domain adaptation via prompt learning for ad detection. we proposed a novel prompt learning paradigm, domain adaptation via prompt-based fine-tuning (dapf), for this purpose. we showed that dapf yields similar performance to the current state-of-the-art @xcite on the adress test set when using a bert base plm and ensembling across three top-performing discrete prompt templates of different forms. we compare dapf across multiple domain adaptation settings, base plms, and prompt template structures relative to strong baselines including p-tuning and switchprompt, a competitive contemporary prompt-based domain adaptation approach. in doing so, we also study the interplay between prompt length, prompt template order, and overall performance. in follow-up analyses, we find that dapf also performs competitively in few-shot settings, achieving performance comparable to full-data training settings when using only 16 target domain training samples. given the widespread data limitations in healthcare tasks such as ad detection, this further supports the utility and anticipated appeal of dapf as a novel training paradigm. we make all source code and models publicly available foot_8 to encourage further experimentation by others.