Lei Jiang
Publications
What Do LLMs Know About Alzheimer's Disease? Fine-Tuning, Probing, and Data Synthesis for AD Detection
Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to limited availability of labeled data. While large language models (LLMs) have shown strong transfer capabilities across domains, adapting them to the AD domain through supervised fine-tuning remains largely unexplored. In this work, we fine-tune an LLM for AD detection and investigate how task-relevant information is encoded within its internal representations. We employ probing techniques to analyze intermediate activations across transformer layers, and we observe that, after fine-tuning, the probing values of specific words and special markers change substantially, indicating that these elements assume a crucial role in the model's improved detection performance. Guided by this insight, we design a curated set of task-aware special markers and train a sequence-to-sequence model as a data-synthesis tool that leverages these markers to generate structurally consistent and diagnostically informative synthetic samples. We evaluate the synthesized data both intrinsically and by incorporating it into downstream training pipelines.
FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering
Faithfulness hallucinations in VQA occur when vision-language models produce fluent yet visually ungrounded answers, severely undermining their reliability in safety-critical applications. Existing detection methods mainly fall into two categories: external verification approaches relying on auxiliary models or knowledge bases, and uncertainty-driven approaches using repeated sampling or uncertainty estimates. The former suffer from high computational overhead and are limited by external resource quality, while the latter capture only limited facets of model uncertainty and fail to sufficiently explore the rich internal signals associated with the diverse failure modes. Both paradigms thus have inherent limitations in efficiency, robustness, and detection performance. To address these challenges, we propose FaithSCAN: a lightweight network that detects hallucinations by exploiting rich internal signals of VLMs, including token-level decoding uncertainty, intermediate visual representations, and cross-modal alignment features. These signals are fused via branch-wise evidence encoding and uncertainty-aware attention. We also extend the LLM-as-a-Judge paradigm to VQA hallucination and propose a low-cost strategy to automatically generate model-dependent supervision signals, enabling supervised training without costly human labels while maintaining high detection accuracy. Experiments on multiple VQA benchmarks show that FaithSCAN significantly outperforms existing methods in both effectiveness and efficiency. In-depth analysis shows hallucinations arise from systematic internal state variations in visual perception, cross-modal reasoning, and language decoding. Different internal signals provide complementary diagnostic cues, and hallucination patterns vary across VLM architectures, offering new insights into the underlying causes of multimodal hallucinations.