Tootfinder

Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration
Jiahe Chen, Jiaying He, Qian Shao, Qiyuan Chen, Jiahe Ying, Hongxia Xu, Jintai Chen, Jianwei Zheng, Jian Wu
https://arxiv.org/abs/2506.21509

Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration
Large Vision-Language Models (LVLMs) have demonstrated significant advancements in multimodal understanding, yet they are frequently hampered by hallucination-the generation of text that contradicts visual input. Existing training-free decoding strategies exhibit critical limitations, including the use of static constraints that do not adapt to semantic drift during generation, inefficiency stemming from the need for multiple forward passes, and degradation of detail due to overly rigid interve…

AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis
Jiale Liu, Dandan Peng, Huan Wang, Chenyu Liu, Yan-Fu Li, Min Xie
https://arxiv.org/abs/2506.16225

AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis
Aerospace engines, as critical components in aviation and aerospace industries, require continuous and accurate fault diagnosis to ensure operational safety and prevent catastrophic failures. While deep learning techniques have been extensively studied in this context, they output logits or confidence scores, necessitating post-processing to derive actionable insights. Furthermore, the potential of large-scale audio models in this domain remains largely untapped. To address these limitations, t…

A break from the norm? Parametric representations of preference heterogeneity for discrete choice models in health
John Buckell, Alice Wreford, Matthew Quaife, Thomas O. Hancock
https://arxiv.org/abs/2506.14099

A break from the norm? Parametric representations of preference heterogeneity for discrete choice models in health
Background: Any sample of individuals has its own, unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used choice model in health. A raft of parametric model specifications for these models are available. We test a range of alternatives assumptions, and model averaging, to test if or how model outputs are impacted. Design: Scoping review of current modelling practices. Seven alternat…

Reducing Object Hallucination in Large Audio-Language Models via Audio-Aware Decoding
Tzu-wen Hsu, Ke-Han Lu, Cheng-Han Chiang, Hung-yi Lee
https://arxiv.org/abs/2506.07233

Reducing Object Hallucination in Large Audio-Language Models via Audio-Aware Decoding
Large Audio-Language Models (LALMs) can take audio and text as the inputs and answer questions about the audio. While prior LALMs have shown strong performance on standard benchmarks, there has been alarming evidence that LALMs can hallucinate what is presented in the audio. To mitigate the hallucination of LALMs, we introduce Audio-Aware Decoding (AAD), a lightweight inference-time strategy that uses contrastive decoding to compare the token prediction logits with and without the audio context…