Cover Image

DiagnoLLM: A Hybrid Bayesian Neural Language Framework for Interpretable Disease Diagnosis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Bowen, Zeng, Xinyue, Hu, Jiazhen, Wang, Tuo, Kulkarni, Adithya
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
by: Wang, Tuo, et al.
Published: (2025)

Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
by: Zhang, Bowen, et al.
Published: (2024)

Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies
by: Kulkarni, Siddhant, et al.
Published: (2026)

Evaluating LLM Understanding via Structured Tabular Decision Simulations
by: Li, Sichao, et al.
Published: (2025)

AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents
by: Xu, Shuyuan, et al.
Published: (2024)

SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
by: Hu, Mengya, et al.
Published: (2024)

Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision Layer
by: Hu, Xinyue, et al.
Published: (2025)

CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
by: Chen, Junying, et al.
Published: (2024)

Interpreting and Controlling LLM Reasoning through Integrated Policy Gradient
by: Li, Changming, et al.
Published: (2026)

Fine-Grained Interpretation of Political Opinions in Large Language Models
by: Hu, Jingyu, et al.
Published: (2025)

Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying
by: Renduchintala, Adithya, et al.
Published: (2023)

AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders
by: Hu, Yuezhou, et al.
Published: (2025)

Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete
by: Rajan, Adithya, et al.
Published: (2025)

SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models
by: He, Zirui, et al.
Published: (2025)

Graph Neural Prompting with Large Language Models
by: Tian, Yijun, et al.
Published: (2023)

NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention
by: Zhang, Tianyi, et al.
Published: (2024)

Thought Branches: Interpreting LLM Reasoning Requires Resampling
by: Macar, Uzay, et al.
Published: (2025)

Towards Specialized Generalists: A Multi-Task MoE-LoRA Framework for Domain-Specific LLM Adaptation
by: Yang, Yuxin, et al.
Published: (2026)

GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM
by: Kang, Hao, et al.
Published: (2024)

ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
by: Wang, Xu, et al.
Published: (2025)

Variational Language Concepts for Interpreting Foundation Language Models
by: Wang, Hengyi, et al.
Published: (2024)

Concept Layers: Enhancing Interpretability and Intervenability via LLM Conceptualization
by: Bidusa, Or Raphael, et al.
Published: (2025)

Robustly Improving LLM Fairness in Realistic Settings via Interpretability
by: Karvonen, Adam, et al.
Published: (2025)

Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models
by: Ji, Xiang, et al.
Published: (2024)

Synergy-of-Thoughts: Eliciting Efficient Reasoning in Hybrid Language Models
by: Shang, Yu, et al.
Published: (2024)

DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders
by: Wang, Xu, et al.
Published: (2026)

Hybrid Quantum-Classical Recurrent Neural Networks
by: Xu, Wenduan
Published: (2025)

KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
by: Zhang, Songming, et al.
Published: (2026)

Martingale Score: An Unsupervised Metric for Bayesian Rationality in LLM Reasoning
by: He, Zhonghao, et al.
Published: (2025)

I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models
by: Hu, Xing, et al.
Published: (2024)

BI-DCGAN: A Theoretically Grounded Bayesian Framework for Efficient and Diverse GANs
by: Valizadeh, Mahsa, et al.
Published: (2025)

MoBayes: A Modular Bayesian Framework for Separating Reasoning from Language in Conversational Clinical Decision Support
by: Kesmen, Yusuf, et al.
Published: (2026)

Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units
by: Chen, Jianhui, et al.
Published: (2026)

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines
by: Gao, Jiechao, et al.
Published: (2026)

A Framework for Inference Inspired by Human Memory Mechanisms
by: Zeng, Xiangyu, et al.
Published: (2023)

Learning to Interpret Weight Differences in Language Models
by: Goel, Avichal, et al.
Published: (2025)

Rethinking Interpretability in the Era of Large Language Models
by: Singh, Chandan, et al.
Published: (2024)

NeSTR: A Neuro-Symbolic Abductive Framework for Temporal Reasoning in Large Language Models
by: Liang, Feng, et al.
Published: (2025)

A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility
by: Madhyastha, Pranava
Published: (2026)

Interpretable by AI Mother Tongue: Native Symbolic Reasoning in Neural Models
by: Liu, Hung Ming
Published: (2025)