arXivDaily arXiv每日学术速递 周一至周五更新

AI 大模型

语言大模型 / LLM

大语言模型、预训练、指令微调、后训练和语言模型应用。

共收录 11511 信号源:cs.CL, cs.AI, cs.LG

1. 指令微调 11511 篇

2503.18681 2025-07-04 cs.CL cs.AI 90%

Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models

Yazhou Zhang, Chunwang Zou, Bo Wang, Jing Qin

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);prompting(abstract);分类 cs.CL、cs.AI

Comments Our original goal was to use Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection (arXiv:2506.19420) to replace Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models (arXiv:2503.18681). Due to various reasons, both versions were released, so we would like to withdraw the latter

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2405.16964 2024-11-11 cs.CL cs.AI 90%

Exploring the LLM Journey from Cognition to Expression with Linear Representations

Yuzi Yan, Jialian Li, Yipin Zhang, Dong Yan

专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);pretraining(abstract)

Comments Published in ICML 2024

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2406.08464 2024-10-08 cs.CL cs.AI 90%

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, Bill Yuchen Lin

专题命中 指令微调 :prompting(title,abstract);LLM(abstract);large language model(abstract);language model(abstract)

Comments Link: https://magpie-align.github.io/

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2309.00363 2023-09-04 cs.LG 90%

FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou

专题命中 指令微调 :LLM(title,abstract);large language model(title);language model(title);分类 cs.LG

Comments Source code: https://github.com/alibaba/FederatedScope/tree/llm

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2507.02778 2026-08-04 cs.CL cs.AI cs.LG 版本更新 90%

Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models

Self-Correction Bench:揭示并解决大语言模型中的自校正盲点

Ken Tsui

机构 * Independent Researcher(独立研究者)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);post-training(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 该研究提出Self-Correction Bench框架,发现大语言模型存在64.5%的自校正盲点,经微调或添加“Wait”可显著降低该盲点,揭示了自校正能力未激活的机制。

Comments Accepted to COLM 2026

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2607.13408 2026-07-28 eess.AS cs.AI cs.CL cs.LG cs.SD 版本更新 90%

Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models

通过音频感知大语言模型的细粒度反馈改进文本到音频的指令跟随

Chun-Yi Kuan, Siwon Kim, Byeonggeun Kim, Suyoun Kim, Bo-Ru Lu, Qingming Tang, Ankur Gandhe, Hung-yi Lee, Chieh-Chi Kao, Chao Wang

机构 * National Taiwan University(国立台湾大学) Amazon(亚马逊)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);preference optimization(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 研究文本到音频指令跟随问题,提出用音频感知大语言模型作细粒度评判器的框架,经验证后用其反馈构建偏好对优化,引入S3Bench基准,实验证明该方法能提升事件完整性、时间排序和指令跟随准确性,且保持音频质量。

Comments Accepted to the Long Paper Track at Interspeech 2026. Project Website: https://kuan2jiu99.github.io/allm-feedback-tta

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2508.01782 2026-07-27 eess.IV cs.CV 版本更新 90%

Joint Lossless Compression and Steganography for Medical Images via Large Language Models

通过大语言模型实现医学图像的联合无损压缩与隐写术

Pengcheng Zheng, Xiaorong Pu, Kecheng Chen, Jiaxin Huang, Meng Yang, Bai Feng, Yazhou Ren, Jianan Jiang, Chaoning Zhang, Yang Yang, Heng Tao Shen

机构 * Center for Future Media and School of Computer Science and Engineering, University of Electronic Science and Technology of China(未来媒体中心和电子科技大学计算机科学与工程学院) Department of Computer Science and Engineering, University of Electronic Science and Technology of China(计算机科学与工程学院,电子科技大学) Department of Electrical Engineering, and the Center for Intelligent Multidimensional Data Analysis, City University of Hong Kong(电子工程系和智能多维数据分析中心,城市大学) Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence(机器学习系,Mohamed bin Zayed人工智能大学)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract,abstract_cn)

AI总结 针对医学图像无损压缩中性能与效率权衡及安全问题,提出联合无损压缩与隐写术框架。基于位平面切片,设计自适应模态分解,创新局部模态路径分段消息隐写术算法,结合A-LoRA微调策略,提升压缩率、效率与安全性。

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2607.13864 2026-07-16 cs.SD 新提交 90%

Rethinking Speech Foundation Model Fine-tuning: Better SFT or Better Match?

重新思考语音基础模型微调:更好的监督微调还是更好的匹配?

Wangjin Zhou, Yizhou Zhang, Yichi Wang, Tatsuya Kawahara

机构 * Graduate School of Informatics, Kyoto University(京都大学信息学研究生院)

专题命中 指令微调 :SFT(title,summary_cn);foundation model(title)

AI总结 研究语音基础模型微调,通过对3个SUPERB分类任务、9个预训练检查点的8种SFT变体进行系统研究,发现SFT结果强烈依赖预训练实例,顶级SFT方法常因检查点而异,下游增益多为实例和种子依赖的匹配,非普遍性能提升。

Comments Accept by Interspeech 2026

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2606.11033 2026-06-10 cs.LG cs.AI cs.CL 新提交 90%

AuRA: Internalizing Audio Understanding into LLMs as LoRA

AuRA: 将音频理解内化到LLM中作为LoRA

Bo Cheng, Lei Shi, Zhanyu Ma, Yuan Wu, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He

机构 * Meituan(美团) Jilin University(吉林大学)

专题命中 指令微调 :LLM(title_cn,summary_cn);large language model(abstract);language model(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 提出AuRA方法,通过层间蒸馏将ASR编码器的语音表示内化到LoRA适配的LLM中,实现紧耦合的语音-语言联合建模和高效并行端到端推理,在多个基准上优于级联系统和现有适应方法。

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2605.29408 2026-05-29 nucl-th 90%

Large language model for unified and accurate description of multidimensional nuclear properties

用于统一准确描述多维核性质的大型语言模型

S. J. Guo, S. Y. Wang, E. H. Wang, Z. M. Niu, Y. M. Ding

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract,abstract_cn)

AI总结 提出一种先验信息增强的大型语言模型多任务学习框架,通过低秩适配微调预训练模型,在电荷半径、质量、结合能、分离能和衰变能等七个可观测量上实现了超过98%的训练损失降低,为核物理多任务回归提供了高效共享方法。

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2505.19075 2026-05-21 cs.AI cs.CL cs.LG 90%

Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs

Universal Reasoner: 一个单一、可组合的即插即用推理器用于冻结的LLM

Jaemin Kim, Hangeol Chang, Hyunmin Hwang, Choonghan Kim, Jong Chul Ye

机构 * Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology(人工智能研究生院,韩国科学技术院)

专题命中 指令微调 :LLM(title_cn,abstract);large language model(abstract,abstract_cn);language model(abstract,abstract_cn);分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出Universal Reasoner,一种可组合且即插即用的推理模块,能够在冻结的大规模语言模型上提供专门的推理能力,通过共享或对齐的token空间实现弱到强的泛化,实验表明其在数学推理和机器翻译中优于现有微调方法。

Comments ICML 2026

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2605.16179 2026-05-18 cs.CV 90%

MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models

MAgSeg:利用多模态大语言模型对高分辨率卫星图像进行农业景观分割

Piyush Tiwary, Utkarsh Ahuja, Depanshu Sani, Aishwarya Jayagopal, Sagar Gubbi, Subhashini Venugopalan, Alok Talekar, Vaibhav Rajan

机构 * Google DeepMind(谷歌DeepMind) Google(谷歌) Indian Institute of Science(印度科学研究院)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);instruction tuning(abstract);post-training(abstract)

AI总结 本文提出MAgSeg,一种无需视觉解码器的多模态大语言模型分割方法,有效解决南半球农业景观分割中的碎片化地块、高类内方差和标注数据稀缺问题,实现高效农业环境制图。

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2604.20148 2026-04-23 cs.CL cs.AI cs.LG 90%

Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models

元工具:用于小语言模型的高效少样本工具适应

Sachin Kumar

机构 * LexisNexis, USA(LexisNexis美国公司)

专题命中 指令微调 :language model(title,abstract);small language model(title,abstract);prompting(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 本文通过Meta-Tool研究小语言模型是否能在不复杂适应机制下实现强工具使用性能,发现超网络适应无显著提升,提示应关注提示工程与示例整理。

Comments Accepted to Findings of ACL 2026

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2604.19321 2026-04-22 cs.LG cs.AI cs.CL cs.CV 90%

RDP LoRA: Geometry-Driven Identification for Parameter-Efficient Adaptation in Large Language Models

RDP LoRA:基于几何的参数高效适应大语言模型的识别

Yusuf Çelebi, Yağız Asker, Özay Ezerceli, Mahmoud ElHussieni, Selva Taş, Reyhan Bayraktar, Fatma Betül Terzioğlu

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract_cn);分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出RDP LoRA方法,通过几何轨迹分析确定关键层进行参数高效微调,实现在MMLU-Math上取得优于全层和随机选择的性能。

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2604.08297 2026-04-10 cs.CR 90%

Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models

向大规模语言模型中安全关键参数的识别与干预迈进

Weiwei Qi, Zefeng Wu, Tianhang Zheng, Zikang Zhang, Xiaojun Jia, Zhan Qin, Kui Ren

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);instruction tuning(abstract)

AI总结 本文提出ESI框架量化不同参数对LLM安全的影响,揭示不同架构中安全关键模式,并引入SET和SPA干预方法提升安全性和稳定性。

Comments 20 pages, 6 figures, 8 tables

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2409.02136 2026-04-10 cs.LG cs.AI cs.CL 90%

Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data

大语言模型与经典机器学习:在使用高维表格数据预测新冠死亡率中的表现

Mohammadreza Ghaffarzadeh-Esfahani, Mahdi Ghaffarzadeh-Esfahani, Arian Salahi-Niri, Hossein Toreyhi, Zahra Atf, Amirali Mohsenzadeh-Kermani, Mahshad Sarikhani, Zohreh Tajabadi, Fatemeh Shojaeian, Mohammad Hassan Bagheri, Aydin Feyzi, Mohammadamin Tarighatpayma, Narges Gazmeh, Fateme Heydari, Hossein Afshar, Amirreza Allahgholipour, Farid Alimardani, Ameneh Salehi, Naghmeh Asadimanesh, Mohammad Amin Khalafi, Hadis Shabanipour, Ali Moradi, Sajjad Hossein Zadeh, Omid Yazdani, Romina Esbati, Moozhan Maleki, Danial Samiei Nasr, Amirali Soheili, Hossein Majlesi, Saba Shahsavan, Alireza Soheilipour, Nooshin Goudarzi, Erfan Taherifard, Hamidreza Hatamabadi, Jamil S Samaan, Thomas Savage, Ankit Sakhuja, Ali Soroush, Girish Nadkarni, Ilad Alavi Darazam, Mohamad Amin Pourhoseingholi, Seyed Amir Ahmad Safavi-Naini

机构 * Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences(沙希德·贝赫什提医科大学胃肠病与肝病研究所) Faculty of Medicine, Isfahan University of Medical Sciences(伊斯法罕医科大学医学院) Faculty of Business and Information Technology, Ontario Tech University(安大略理工大学商业与信息技术学院) School of Medicine, Shahid Beheshti University of Medical Sciences(沙希德·贝赫什提医科大学医学院) Digestive Disease Research Institute, Tehran University of Medical Sciences(德黑兰医科大学消化疾病研究所) Department of Surgery, The Johns Hopkins University(约翰霍普金斯大学外科学系) Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences(沙希德·贝赫什提医科大学护理与助产学院学生研究委员会) MPH department, Shiraz University of Medical Sciences(设拉子医科大学公共卫生硕士系) Department of Emergency Medicine, School of Medicine, Safety Promotion and Injury Prevention Research Center, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences(沙希德·贝赫什提医科大学伊玛目侯赛因医院医学院急诊医学系安全促进与伤害预防研究中心) Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center(西达赛奈医疗中心卡什胃肠病与肝病科) Department of Medicine, Stanford University(斯坦福大学医学系) Division of Data Driven and Digital Health (D3M), The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai(西奈山伊坎医学院查尔斯·布朗夫曼个性化医学研究所数据驱动与数字健康部) Infectious Diseases and Tropical Medicine Research Center, Shahid Beheshti University of Medical Sciences(沙希德·贝赫什提医科大学传染病与热带医学研究中心) Department of Infectious Diseases, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences(沙希德·贝赫什提医科大学洛格曼·哈基姆医院传染病科) National Institute for Health and Care Research (NIHR), Nottingham Biomedical Research Centre, Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham(诺丁汉大学医学院国家健康与护理研究所诺丁汉生物医学研究中心听力科学、心理健康与临床神经科学)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 本文比较了经典特征机器学习模型与大语言模型在预测新冠死亡率中的性能,发现经典模型在处理高维表格数据方面仍占优势,但通过微调大语言模型可显著提升其效果。

Comments Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

Journal ref Scientific Reports 15, 42712 (2025)

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2604.01762 2026-04-03 cs.LG cs.AI cs.CL cs.DC 90%

FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models

FourierMoE:大型语言模型的傅里叶混合专家适应

Juyong Jiang, Fan Wang, Hong Qi, Sunghun Kim, Jing Tang

机构 * The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州))

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出傅里叶MoE,通过频域分析提升大语言模型在多任务适应中的性能,采用频域适应方法减少参数开销,实验证明其在单任务和多任务场景中均优于基线方法。

Comments The first two authors contributed equally to this work; listing order is random

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2506.13734 2026-03-27 cs.CL cs.AI cs.LG 90%

Instruction Following by Principled Boosting Attention of Large Language Models

通过原理性提升大语言模型的注意力来实现指令遵循

Vitoria Guardieiro, Avishree Khare, Adam Stein, Eric Wong

机构 * University of Pennsylvania(宾夕法尼亚大学)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);prompting(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出InstABoost方法,通过提升指令相关注意力来增强指令遵循,避免了其他方法的缺陷,提升了指令引导与任务相关上下文的平衡。

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2503.22764 2026-03-17 cs.CL cs.AI cs.LG 90%

Boosting Large Language Models with Mask Fine-Tuning

通过掩码微调提升大语言模型

Mingyuan Zhang, Yue Bai, Huan Wang, Yizhou Wang, Qihua Dong, Yitian Zhang, Yun Fu

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出掩码微调方法,通过破坏模型结构完整性提升性能,适用于多种领域和模型架构,实现平均2.70/4.15的提升。

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2506.15751 2026-03-09 cs.AI cs.CL cs.LG 90%

Sysformer: Safeguarding Frozen Large Language Models with Adaptive System Prompts

Sysformer: 通过自适应系统提示保护冻结的大语言模型

Kartik Sharma, Yiqiao Jin, Vineeth Rakesh, Yingtong Dou, Menghai Pan, Mahashweta Das, Srijan Kumar

机构 * Georgia Institute of Technology(佐治亚理工学院) Visa Research(Visa研究)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 Sysformer通过自适应系统提示提高大语言模型的安全性,显著提升对有害提示的拒绝率和对安全提示的合规性。

Comments ICLR 2026. Code available at https://github.com/Ksartik/sysformer

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2410.04010 2026-02-09 cs.LG cs.AI cs.CL cs.NE 90%

Hyperbolic Fine-Tuning for Large Language Models

双曲微调用于大语言模型

Menglin Yang, Ram Samarth B B, Aosong Feng, Bo Xiong, Jihong Liu, Irwin King, Rex Ying

机构 * HKUST(GZ)(香港科技大学(广州)) HKUST(香港科技大学) Indian Institute of Science(印度科学研究院) Yale University(耶鲁大学) Stanford University(斯坦福大学) The Chinese University of Hong Kong(香港中文大学)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 HypLoRA通过在双曲空间中进行低秩适应,提升大语言模型在算术和常识推理任务中的性能。

Comments NeurIPS 2025; https://github.com/marlin-codes/HypLoRA

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2402.12819 2026-01-26 cs.CL cs.AI cs.LG 90%

Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance

比较专门化的小型模型和通用的大型模型在文本分类中的表现:100个标注样本实现平衡性能

Branislav Pecher, Ivan Srba, Maria Bielikova

机构 * Faculty of Information Technology, Brno University of Technology(信息技术学院,布拉格技术大学) Kempelen Institute of Intelligent Technologies(智能技术研究所)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);prompting(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 研究比较了专门化小型模型与通用大型模型在文本分类中的表现,发现平均100个标注样本即可实现平衡性能,且样本需求受任务特征和方差影响显著。

Comments Accepted to the EMNLP 2025 conference

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2508.04748 2026-01-21 cs.LG cs.AI cs.CL 90%

AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models

AttriLens-Mol:基于属性引导的强化学习用于利用大语言模型进行分子属性预测

Xuan Lin, Long Chen, Yile Wang

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);prompting(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 AttriLens-Mol通过属性引导的强化学习框架提升大语言模型在分子属性预测中的性能,有效激发相关属性,提高可解释性和预测效果。

Comments 9 pages

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2509.23773 2026-01-16 cs.LG cs.AI cs.CL cs.SI 90%

Knowledge Homophily in Large Language Models

大型语言模型中的知识同质性

Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar, Nedim Lipka, Ryan A. Rossi, Franck Dernoncourt, Yu Zhang, Yao Ma, Yu Wang

机构 * University of Oregon Eugene OR USA Pacific Northwest National Laboratory Richland WA USA Adobe Research San Jose CA USA Texas A\&M University College Station TX USA Rensselaer Polytechnic Institute Troy NY USA University of Oregon Pacific Northwest National Laboratory Adobe Research Texas A\&M University Rensselaer Polytechnic Institute

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出了一种基于图神经网络的模型,通过分析LLM知识的同质性,提高知识标注效率和推理问答性能。

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2601.08141 2026-01-14 cs.CL cs.AI cs.LG 90%

Qalb: Largest State-of-the-Art Urdu Large Language Model for 230M Speakers with Systematic Continued Pre-training

Qalb:面向2.3亿使用者的最先进的乌尔都语大型语言模型,采用系统性持续预训练

Muhammad Taimoor Hassan, Jawad Ahmed, Muhammad Awais

机构 * Auburn University, USA(美国阿伯杜大学) BHT Berlin, Germany(柏林BHT学院) BTU Cottbus, Germany(库滕堡工业大学)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);foundation model(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 Qalb通过持续预训练和监督微调,为乌尔都语构建了最先进的大型语言模型,显著提升了在多种任务上的性能。

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2512.02584 2025-12-03 cs.MM 90%

Stepwise Schema-Guided Prompting Framework with Parameter Efficient Instruction Tuning for Multimedia Event Extraction

分步模式引导提示框架与参数高效指令微调用于多模态事件提取

Xiang Yuan, Xinrong Chen, Haochen Li, Hang Yang, Guanyu Wang, Weiping Li, Tong Mo

专题命中 指令微调 :instruction tuning(title,abstract);prompting(title,abstract);large language model(abstract);language model(abstract)

AI总结 本文提出分步模式引导提示框架与参数高效指令微调方法,用于提升多模态事件提取任务的性能。

Comments Accepted by 2025 IEEE International Conference on Multimedia and Expo

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2412.15287 2025-11-27 cs.CL cs.AI cs.LG 90%

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models

为大型语言模型的最佳-N采样进行推理感知微调

Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral Kumar, Aleksandra Faust

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出了一种推理感知微调方法,通过改进最佳-N采样策略提升大型语言模型的性能和推理效率。

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2510.17895 2025-10-22 cs.LG cs.AI cs.CL 90%

Hierarchical Federated Unlearning for Large Language Models

Yisheng Zhong, Zhengbang Yang, Zhuangdi Zhu

机构 * George Mason University(乔治·玛莎大学)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

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2510.05442 2025-10-08 cs.LG cs.AI cs.CL 90%

Adversarial Reinforcement Learning for Large Language Model Agent Safety

Zizhao Wang, Dingcheng Li, Vaishakh Keshava, Phillip Wallis, Ananth Balashankar, Peter Stone, Lukas Rutishauser

机构 * Google(谷歌) Google Deepmind(谷歌DeepMind) The University of Texas at Austin(德克萨斯大学奥斯汀分校) Sony AI(索尼人工智能)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);LLM(abstract);分类 cs.CL、cs.AI、cs.LG

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2412.05723 2025-09-29 stat.ML cs.AI cs.CL cs.LG 90%

Training-Free Bayesianization for Low-Rank Adapters of Large Language Models

Haizhou Shi, Yibin Wang, Ligong Han, Huan Zhang, Hao Wang

机构 * Rutgers University(新泽西罗格斯大学) University of Illinois Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校) Red Hat AI Innovation(红帽AI创新)

专题命中 指令微调 :large language model(title,abstract);language model(title,abstract);post-training(abstract);分类 cs.CL、cs.AI、cs.LG

Comments Accepted at NeurIPS 2025

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