MMSE-Calibrated Few-Shot Prompting for Alzheimer's Detection

• URL: http://arxiv.org/abs/2509.19926v1
• Date: Wed, 24 Sep 2025 09:33:46 GMT
• Title: MMSE-Calibrated Few-Shot Prompting for Alzheimer's Detection
• Abstract summary: We study few-shot prompting with a class-balanced protocol using nested interleave and a strict schema.<n>We evaluate two variants achieving state-of-the-art prompting results.<n>This is the first ADReSS study to anchor elicited probabilities to MMSE and to use multimodal construction to improve interpretability.
• Score: 4.176236534741941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompting large language models is a training-free method for detecting Alzheimer's disease from speech transcripts. Using the ADReSS dataset, we revisit zero-shot prompting and study few-shot prompting with a class-balanced protocol using nested interleave and a strict schema, sweeping up to 20 examples per class. We evaluate two variants achieving state-of-the-art prompting results. (i) MMSE-Proxy Prompting: each few-shot example carries a probability anchored to Mini-Mental State Examination bands via a deterministic mapping, enabling AUC computing; this reaches 0.82 accuracy and 0.86 AUC (ii) Reasoning-augmented Prompting: few-shot examples pool is generated with a multimodal LLM (GPT-5) that takes as input the Cookie Theft image, transcript, and MMSE to output a reasoning and MMSE-aligned probability; evaluation remains transcript-only and reaches 0.82 accuracy and 0.83 AUC. To our knowledge, this is the first ADReSS study to anchor elicited probabilities to MMSE and to use multimodal construction to improve interpretability.

Related papers

• Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models [59.51249894128724]
  Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration.<n>We propose ADAS, a training-free reranking rule for parallel masked diffusion decoding.
  arXiv  Detail & Related papers  (2026-06-09T13:17:27Z)
• An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic [16.151531550259993]
  Large language models (LLMs) are increasingly deployed through hosted APIs, making model extraction a practical threat to model ownership and service security.<n>We formulate model extraction monitoring as benign-calibrated traffic-window distribution testing.<n>We show that an embarrassingly simple detector is effective: embed incoming queries into a semantic space and test whether their aggregate distribution deviates from historical benign traffic.
  arXiv  Detail & Related papers  (2026-06-04T05:33:49Z)
• Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models [9.896454972048376]
  Large language models (LLMs) offer new possibilities for automated mental illness phenomenology.<n>We present a novel automated, multi-agent LLM pipeline for the fine-grained, multi-label extraction of language suggestive of delusional beliefs.<n>This work provides a validated and scalable pipeline for the automated detection and characterization of content suggesting delusional beliefs in naturalistic speech.
  arXiv  Detail & Related papers  (2026-05-23T22:18:40Z)
• Can Large Language Models Revolutionize Survey Research? Experiments with Disaster Preparedness Responses [6.004875368104112]
  Large language models (LLMs) have been proposed as a remedy, yet rigorous evaluations across the full survey workflow remain scarce.<n>We present and evaluate a five-stage framework for LLM integration covering questionnaire design, sample selection, pilot testing, missing-data imputation, and post-collection analysis.<n>We introduce a Protection Motivation Theory (PMT)-constrained co-occurrence knowledge graph and develop seven LLM configurations spanning zero-shot inference, retrieval-augmented baselines, and novel theory-informed variants.
  arXiv  Detail & Related papers  (2026-05-19T00:58:36Z)
• Membership Inference Attacks against Large Audio Language Models [50.84901010528239]
  We present the first systematic Membership Inference Attack (MIA) evaluation of Large Audio Language Models (LALMs)<n>Using a multi-modal blind baseline based on textual, spectral, and prosodic features, we demonstrate that common speech datasets exhibit near-perfect train/test separability.<n>These findings establish a principled standard for auditing LALMs beyond spurious correlations.
  arXiv  Detail & Related papers  (2026-03-30T12:45:28Z)
• From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs [58.02809208460186]
  We revisit this paradox using high-quality traces from DeepSeek-R1 as demonstrations.<n>We find that adding more exemplars consistently degrades accuracy, even when demonstrations are optimal.<n>We introduce Insight-to-solve (I2S), a sequential test-time procedure that turns demonstrations into explicit, reusable insights.
  arXiv  Detail & Related papers  (2025-09-27T08:59:31Z)
• SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models [74.40683913645731]
  Zero-shot multi-label recognition (MLR) with Vision-Language Models (VLMs) faces significant challenges without training data, model tuning, or architectural modifications.<n>Our work proposes a novel solution treating VLMs as black boxes, leveraging scores without training data or ground truth.<n>Analysis of these prompt scores reveals VLM biases and AND''/OR' signal ambiguities, notably that maximum scores are surprisingly suboptimal compared to second-highest scores.
  arXiv  Detail & Related papers  (2025-02-24T07:15:05Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method [11.069975459609829]
  We propose a low-level Mispronunciation Detection and Diagnosis (MDD) approach based on the detection of speech attribute features. The proposed method was applied to L2 speech corpora collected from English learners from different native languages.
  arXiv  Detail & Related papers  (2023-11-13T02:41:41Z)
• Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data [55.84746218227712]
  This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
  arXiv  Detail & Related papers  (2023-08-09T07:51:40Z)
• Masked Language Model Based Textual Adversarial Example Detection [14.734863175424797]
  Adrial attacks are a serious threat to reliable deployment of machine learning models in safety-critical applications. We propose a novel textual adversarial example detection method, namely Masked Model-based Detection (MLMD)
  arXiv  Detail & Related papers  (2023-04-18T06:52:14Z)
• DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature [143.5381108333212]
  We show that text sampled from an large language model tends to occupy negative curvature regions of the model's log probability function. We then define a new curvature-based criterion for judging if a passage is generated from a given LLM. We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection.
  arXiv  Detail & Related papers  (2023-01-26T18:44:06Z)
• CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning [10.876763955414576]
  This work explores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples.<n>First, we pair self-supervised contrastive learning with the maximum mean discrepancy (MMD) two-sample test.<n>Motivated by this success, we introduce CADet, a novel method for OOD detection of single samples.
  arXiv  Detail & Related papers  (2022-10-04T17:02:37Z)
• IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach [3.4423596432619754]
  We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM) We compare the performance of this method against ensemble techniques trained on the entire dataset.
  arXiv  Detail & Related papers  (2022-09-08T16:03:50Z)
• Sequence-level self-learning with multiple hypotheses [53.04725240411895]
  We develop new self-learning techniques with an attention-based sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR) In contrast to conventional unsupervised learning approaches, we adopt the emphmulti-task learning (MTL) framework. Our experiment results show that our method can reduce the WER on the British speech data from 14.55% to 10.36% compared to the baseline model trained with the US English data only.
  arXiv  Detail & Related papers  (2021-12-10T20:47:58Z)
• Incremental Verification of Fixed-Point Implementations of Neural Networks [0.19573380763700707]
  We develop and evaluate a novel symbolic verification framework using incremental bounded model checking (BMC), satisfiability modulo theories (SMT), and invariant inference. Our approach was able to verify and produce examples for 85.8% of 21 test cases considering different input images, and 100% of the properties related to covering methods.
  arXiv  Detail & Related papers  (2020-12-21T10:03:44Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.