Delta — Contrastive Decoding Mitigates Text Hallucinations in Large Language Models

Kavli Affiliate: Cheng Peng | First 5 Authors: Cheng Peng Huang, Hao-Yuan Chen, , , | Summary: Large language models (LLMs) demonstrate strong capabilities in natural language processing but remain prone to hallucinations, generating factually incorrect or fabricated content. This issue undermines their reliability, particularly in high-stakes domains such as healthcare and legal advisory. To […]


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Superconducting Properties of the Titanium-Based Oxides Compounds: A Review

Kavli Affiliate: Yi Zhou | First 5 Authors: Junqi He, Yi Zhou, , , | Summary: In recent years, the superconductivity of novel layered materials, titanium-based pnictide oxides, was discovered. Due to the properties of possessing both cuprate and iron-based superconductors, these compounds have attracted the interest of researchers. Titanium pnictide oxides were reported to […]


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MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin

Kavli Affiliate: Yi Zhou | First 5 Authors: Minrui Chen, Yi Zhou, Huidong Jiang, Yuhan Zhu, Guanjie Zou | Summary: Fever of unknown origin FUO remains a diagnostic challenge. MedMimic is introduced as a multimodal framework inspired by real-world diagnostic processes. It uses pretrained models such as DINOv2, Vision Transformer, and ResNet-18 to convert high-dimensional […]


Continue.. MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin

MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of Fever of Unknown Origin

Kavli Affiliate: Yi Zhou | First 5 Authors: Minrui Chen, Yi Zhou, Huidong Jiang, Yuhan Zhu, Guanjie Zou | Summary: Fever of unknown origin FUO remains a diagnostic challenge. MedMimic is introduced as a multimodal framework inspired by real-world diagnostic processes. It uses pretrained models such as DINOv2, Vision Transformer, and ResNet-18 to convert high-dimensional […]


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Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization

Kavli Affiliate: Cheng Peng | First 5 Authors: Yuanye Liu, Jiahang Xu, Li Lyna Zhang, Qi Chen, Xuan Feng | Summary: Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design. While recent research has focused on optimizing prompt content, the role of prompt formatting, […]


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Psychometric-Based Evaluation for Theorem Proving with Large Language Models

Kavli Affiliate: Long Zhang | First 5 Authors: Jianyu Zhang, Yongwang Zhao, Long Zhang, Jilin Hu, Xiaokun Luan | Summary: Large language models (LLMs) for formal theorem proving have become a prominent research focus. At present, the proving ability of these LLMs is mainly evaluated through proof pass rates on datasets such as miniF2F. However, […]


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MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining

Kavli Affiliate: Yi Zhou | First 5 Authors: Ruiqi Wu, Na Su, Chenran Zhang, Tengfei Ma, Tao Zhou | Summary: Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievements, they significantly rely on large-scale private image-text data but pay less attention to […]


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GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search

Kavli Affiliate: Cheng Peng | First 5 Authors: Fuchuan Qu, Cheng Peng, Jiaojiao Guan, Donglin Wang, Yanni Sun | Summary: Motivation: Nucleocytoplasmic large DNA viruses (NCLDVs) are notable for their large genomes and extensive gene repertoires, which contribute to their widespread environmental presence and critical roles in processes such as host metabolic reprogramming and nutrient […]


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A Post-Processing-Based Fair Federated Learning Framework

Kavli Affiliate: Yi Zhou | First 5 Authors: Yi Zhou, Naman Goel, , , | Summary: Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server. However, the distributed nature of FL poses challenges in training fair federated learning models. The existing techniques are often limited in […]


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A Post-Processing-Based Fair Federated Learning Framework

Kavli Affiliate: Yi Zhou | First 5 Authors: Yi Zhou, Naman Goel, , , | Summary: Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server. However, the distributed nature of FL poses challenges in training fair federated learning models. The existing techniques are often limited in […]


Continue.. A Post-Processing-Based Fair Federated Learning Framework