Author name cluster

Ran Song

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAAI Conference 2026 Conference Paper

Consensus-Aligned Neuron Efficient Fine-Tuning Large Language Models for Multi-Domain Machine Translation

Shuting Jiang
Ran Song
Yuxin Huang
Yan Xiang
Yantuan Xian
Shengxiang Gao
Zhengtao Yu

Multi-domain machine translation (MDMT) aims to build a unified model capable of translating content across diverse domains. Despite the impressive machine translation capabilities demonstrated by large language models (LLMs), domain adaptation still remains a challenge for LLMs. Existing MDMT methods such as in-context learning and parameter-efficient fine-tuning often suffer from domain shift, parameter interference and limited generalization. In this work, we propose a neuron-efficient fine-tuning framework for MDMT that identifies and updates consensus-aligned neurons within LLMs. These neurons are selected by maximizing the mutual information between neuron behavior and domain features, enabling LLMs to capture both generalizable translation patterns and domain-specific nuances. Our method then fine-tunes LLMs guided by these neurons, effectively mitigating parameter interference and domain-specific overfitting. Comprehensive experiments on three LLMs across ten German-English and Chinese-English translation domains evidence that our method consistently outperforms strong PEFT baselines on both seen and unseen domains, achieving state-of-the-art performance.

PDF Details DOI

JBHI Journal 2026 Journal Article

Heterophily-Aware Spectral GCN for Population-Level Brain Disorder Prediction

Hao Zhang
Liping Wang
Yitian Zhao
Jianyang Xie
Tiyu Fang
Ran Song
Wei Zhang

Integrating resting-state functional magnetic resonance imaging (rs-fMRI) and phenotypic data is a promising way to build a comprehensive population graph for the prediction of brain disorders using graph neural networks (GNNs). However, existing GNN-based methods face two limitations: the complexity of relationships between subjects poses challenges in constructing a well-defined population graph, and the inherent node heterophily within the population graph is often overlooked. To address them, we propose a population graph with a phenotypic encoder, which leverages rs-fMRI and phenotypic data to model complex relationships between subjects and enables GNN to learn population-level features. We also design a heterophily-aware spectral graph convolution network that incorporates local similarity-based learning to assess node homophily and addresses the heterophily issue. Experiments demonstrate that our method performs well in classifying both Alzheimer's Disease and Autism Spectrum Disorder. In addition, it can distinguish between progressive and stable mild cognitive impairment, facilitating timely interventions for the diseases.

Details DOI

IJCAI Conference 2025 Conference Paper

SetKE: Knowledge Editing for Knowledge Elements Overlap

Yifan Wei
Xiaoyan Yu
Ran Song
Hao Peng
Angsheng Li

Large Language Models (LLMs) excel in tasks such as retrieval and question answering but require updates to incorporate new knowledge and reduce inaccuracies and hallucinations. Traditional updating methods, like fine-tuning and incremental learning, face challenges such as overfitting and high computational costs. Knowledge Editing (KE) provides a promising alternative but often overlooks the Knowledge Element Overlap (KEO) phenomenon, where multiple triplets share common elements, leading to editing conflicts. We identify the prevalence of KEO in existing KE datasets and show its significant impact on current KE methods, causing performance degradation in handling such triplets. To address this, we propose a new formulation, Knowledge Set Editing (KSE), and introduce SetKE, a method that edits sets of triplets simultaneously. Experimental results demonstrate that SetKE outperforms existing methods in KEO scenarios on mainstream LLMs. Additionally, we introduce EditSet, a dataset containing KEO triplets, providing a comprehensive benchmark.

PDF Details DOI

NeurIPS Conference 2020 Conference Paper

Online Decision Based Visual Tracking via Reinforcement Learning

Ke Song
Wei Zhang
Ran Song
Yibin Li

A deep visual tracker is typically based on either object detection or template matching while each of them is only suitable for a particular group of scenes. It is straightforward to consider fusing them together to pursue more reliable tracking. However, this is not wise as they follow different tracking principles. Unlike previous fusion-based methods, we propose a novel ensemble framework, named DTNet, with an online decision mechanism for visual tracking based on hierarchical reinforcement learning. The decision mechanism substantiates an intelligent switching strategy where the detection and the template trackers have to compete with each other to conduct tracking within different scenes that they are adept in. Besides, we present a novel detection tracker which avoids the common issue of incorrect proposal. Extensive results show that our DTNet achieves state-of-the-art tracking performance as well as good balance between accuracy and efficiency. The project website is available at https: //vsislab. github. io/DTNet/.

PDF Details