Cover Image

What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Lei, Zhou, Yue, Parde, Natalie
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fine-Tuning Language Models to Know What They Know
by: Park, Sangjun, et al.
Published: (2026)

Context-Aware Counterfactual Data Augmentation for Gender Bias Mitigation in Language Models
by: Parihar, Shweta, et al.
Published: (2026)

What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
by: Lee, Joosung, et al.
Published: (2026)

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
by: Zhao, Yukun, et al.
Published: (2023)

Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory
by: Cacioli, Jon-Paul
Published: (2026)

Do Large Language Models Know What They Are Capable Of?
by: Barkan, Casey O., et al.
Published: (2025)

AD-GPT: Large Language Models in Alzheimer's Disease
by: Liu, Ziyu, et al.
Published: (2025)

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
by: Lyu, Yougang, et al.
Published: (2024)

A Primer in Post-Training Reasoning Data: What We Know About How It Works
by: Li, Yaoming, et al.
Published: (2026)

KnowRL: Teaching Language Models to Know What They Know
by: Kale, Sahil, et al.
Published: (2025)

Do Retrieval Augmented Language Models Know When They Don't Know?
by: Zhou, Youchao, et al.
Published: (2025)

What About the Scene with the Hitler Reference? HAUNT: A Framework to Probe LLMs' Self-consistency Via Adversarial Nudge
by: Dutta, Arka, et al.
Published: (2025)

BertaQA: How Much Do Language Models Know About Local Culture?
by: Etxaniz, Julen, et al.
Published: (2024)

Can AI Assistants Know What They Don't Know?
by: Cheng, Qinyuan, et al.
Published: (2024)

Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations
by: Liu, Yilong, et al.
Published: (2026)

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
by: Li, Wu, et al.
Published: (2026)

Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs
by: Kansal, Yuval, et al.
Published: (2025)

AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
by: Yang, Tiankai, et al.
Published: (2025)

Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
by: Pan, Wenbo, et al.
Published: (2025)

Natural Language Fine-Tuning
by: Liu, Jia, et al.
Published: (2024)

Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
by: Arteaga, Gabriel Y., et al.
Published: (2024)

CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts
by: Nguyen, Hoang H., et al.
Published: (2024)

Fine-Tuning LLMs for Reliable Medical Question-Answering Services
by: Anaissi, Ali, et al.
Published: (2024)

Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG
by: Chen, Xinxi, et al.
Published: (2024)

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
by: Mei, Zhiting, et al.
Published: (2025)

RankAdaptor: Hierarchical Rank Allocation for Efficient Fine-Tuning Pruned LLMs via Performance Model
by: Zhou, Changhai, et al.
Published: (2024)

Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon
by: Cohen-Inger, Nurit, et al.
Published: (2025)

EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs
by: Guo, Song, et al.
Published: (2024)

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
by: Chen, Zihong, et al.
Published: (2025)

Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?
by: Zhong, Chengzhi, et al.
Published: (2024)

When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
by: McGovern, Hope, et al.
Published: (2026)

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
by: Ni, Yuansheng, et al.
Published: (2025)

Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
by: Maggini, Michele Joshua, et al.
Published: (2025)

Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?
by: Zhang, Yue, et al.
Published: (2026)

Fine-Tuning LLMs for Low-Resource Dialect Translation: The Case of Lebanese
by: Yakhni, Silvana, et al.
Published: (2025)

Supervised Fine-Tuning or In-Context Learning? Evaluating LLMs for Clinical NER
by: Baroian, Andrei
Published: (2025)

Incivility and Rigidity: Evaluating the Risks of Fine-Tuning LLMs for Political Argumentation
by: Churina, Svetlana, et al.
Published: (2024)

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs
by: Kothari, Sara, et al.
Published: (2025)

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs
by: Nuti, Felipe, et al.
Published: (2025)

60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering
by: Ye, Junjie, et al.
Published: (2024)