arXiv subjects

Jinyuan Luo

Publications and source records attributed to Jinyuan Luo.

3 recordsLinked to original sources

arXiv2026

OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios

Hallucination detection is essential for the reliable deployment of large language models (LLMs). However, existing evaluations face two core challenges: inconsistent inference configuration and evaluation, and limited coverage of downstream domains and tasks. Consequently, reported detector performance is often difficult to compare, reproduce, and generalize beyond specific experimental settings. We introduce OpenHalDet, a unified benchmark for hallucination detection across diverse generation scenarios. OpenHalDet standardizes the evaluation pipeline, from prompt construction and response generation to truthfulness annotation, detector scoring, and metric computation. It supports heterogeneous detector families under different access settings, including black-box methods that use only generated outputs, gray-box methods that rely on probability-based signals, and white-box methods that exploit internal model signals. By bringing diverse tasks, models, and detectors into a shared framework, OpenHalDet enables controlled comparison and provides a systematic view of how different detection paradigms behave in LLM applications. We release OpenHalDet as an open and extensible codebase to facilitate reproducible evaluation and future development of hallucination detection methods. The code and datasets are available at https://github.com/Nellie179/Hallucination-Detection.

cs.CL↗

arXiv2025

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations

Hallucination remains a key obstacle to the reliable deployment of large language models (LLMs) in real-world question answering tasks. A widely adopted strategy to detect hallucination, known as self-assessment, relies on the model's own output confidence to estimate the factual accuracy of its answers. However, this strategy assumes that the model's output distribution closely reflects the true data distribution, which may not always hold in practice. As bias accumulates through the model's layers, the final output can diverge from the underlying reasoning process, making output-level confidence an unreliable signal for hallucination detection. In this work, we propose Sample-Specific Prompting (SSP), a new framework that improves self-assessment by analyzing perturbation sensitivity at intermediate representations. These representations, being less influenced by model bias, offer a more faithful view of the model's latent reasoning process. Specifically, SSP dynamically generates noise prompts for each input and employs a lightweight encoder to amplify the changes in representations caused by the perturbation. A contrastive distance metric is then used to quantify these differences and separate truthful from hallucinated responses. By leveraging the dynamic behavior of intermediate representations under perturbation, SSP enables more reliable self-assessment. Extensive experiments demonstrate that SSP significantly outperforms prior methods across a range of hallucination detection benchmarks.

cs.AI↗

arXiv2024

Advancing glaucoma research with multiphysics continuum mechanics modelling: Opportunities and open challenges

This review examines the emerging role of mechanistic mathematical models based on continuum mechanics to address current challenges in glaucoma research. At present, the advent of Artificial Intelligence and data-based models have resulted in significant progress in drug candidate screening, target identification and delivery optimization for glaucoma treatment. Physics-based models on the other hand offer mechanistic insight by modelling fundamental physical knowledge. Mechanistic models, and specifically those based on continuum mechanics, have the potential to contribute to a better understanding of glaucoma through the description of intraocular fluid dynamics, mass and heat transfer and other basic physical phenomena. So far, these models have expanded our understanding of ocular fluid dynamics, including descriptions of fluid flow profiles within the anterior chamber of the eye under glaucomatous conditions. With the ongoing development of multiphysics modelling frameworks, there is increasing potential to apply these tools to a wide range of current challenges within the field of glaucoma research. These challenges include glaucoma drainage devices, minimally invasive surgical procedures, therapeutic contact lenses, laser-based interventions like peripheral iridotomy, and the design and optimization of biodegradable drug-releasing intracameral implants, which support patient-specific strategies for glaucoma diagnosis and treatment

q-bio.TO↗