大模型对齐与安全

Enhancing Guardrails for Safe and Secure Healthcare AI

Ananya Gangavarapu

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.AI

EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models

Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An, Weihao Chen, Chunhui Li, Jianbing Zhang, Xinyu Dai

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Knowledge Verification to Nip Hallucination in the Bud

Fanqi Wan, Xinting Huang, Leyang Cui, Xiaojun Quan, Wei Bi, Shuming Shi

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Comments Accepted to EMNLP 2024 (Main Conference)

TACE: Tumor-Aware Counterfactual Explanations

Eleonora Beatrice Rossi, Eleonora Lopez, Danilo Comminiello

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.LG

Comments The paper has been accepted at Italian Workshop on Neural Networks (WIRN) 2024

Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing

Swastika Roy, Hatim Chergui, Christos Verikoukis

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.LG

Comments Submitted for possible publication in IEEE

Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

Xinran Zhao, Hongming Zhang, Xiaoman Pan, Wenlin Yao, Dong Yu, Tongshuang Wu, Jianshu Chen

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.CL

Comments 17 pages, 10 figures

Journal ref Findings of the Association for Computational Linguistics ACL 2024

Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction

Amir Mukeri, Habibullah Shaikh, D. P. Gaikwad

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.LG

Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics

Weijia Zhang, Mohammad Aliannejadi, Yifei Yuan, Jiahuan Pei, Jia-Hong Huang, Evangelos Kanoulas

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Comments Accepted by the 17th International Natural Language Generation Conference (INLG 2024) as an oral presentation

A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring

Vispi Karkaria, Jie Chen, Christopher Luey, Chase Siuta, Damien Lim, Robert Radulescu, Wei Chen

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.LG

Comments Paper accepted at ASME IDETC 2024, and fast-tracked for ASME Journal of Computing and Information Science in Engineering

Best Practices and Lessons Learned on Synthetic Data

Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, Andrew M. Dai

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.CL

Comments In COLM 2024

Probabilistic Scoring Lists for Interpretable Machine Learning

Jonas Hanselle, Stefan Heid, Johannes Fürnkranz, Eyke Hüllermeier

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.LG

Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models

Zhengxuan Wu, Yuhao Zhang, Peng Qi, Yumo Xu, Rujun Han, Yian Zhang, Jifan Chen, Bonan Min, Zhiheng Huang

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Comments preprint

Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors

Emma Cramer, Bernd Frauenknecht, Ramil Sabirov, Sebastian Trimpe

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.LG

Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images

Yuanyuan Peng, Aidi Lin, Meng Wang, Tian Lin, Ke Zou, Yinglin Cheng, Tingkun Shi, Xulong Liao, Lixia Feng, Zhen Liang, Xinjian Chen, Huazhu Fu, Haoyu Chen

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.AI

Comments All codes are available at https://github.com/yuanyuanpeng0129/FMUE

Deepfake tweets automatic detection

Adam Frej, Adrian Kaminski, Piotr Marciniak, Szymon Szmajdzinski, Soveatin Kuntur, Anna Wroblewska

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.CL

Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA

Zehuan Zhang, Hongxiang Fan, Hao Mark Chen, Lukasz Dudziak, Wayne Luk

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.LG

Comments Design Automation Conference (DAC) 2024

Are Logistic Models Really Interpretable?

Danial Dervovic, Freddy Lécué, Nicolás Marchesotti, Daniele Magazzeni

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.LG

Comments 36 pages, 5 Figures. Extended version of paper accepted to IJCAI 2024. arXiv admin note: substantial text overlap with arXiv:2211.06360

Improving Multi-Agent Debate with Sparse Communication Topology

Yunxuan Li, Yibing Du, Jiageng Zhang, Le Hou, Peter Grabowski, Yeqing Li, Eugene Ie

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Comments 13 pages, 9 figures

Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs

Shang Zhou, Feng Yao, Chengyu Dong, Zihan Wang, Jingbo Shang

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Comments Accepted to ACL 2024 Findings

Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification

Shanshan Xu, T. Y. S. S Santosh, Oana Ichim, Barbara Plank, Matthias Grabmair

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection

Qutub Syed, Michael Paulitsch, Korbinian Hagn, Neslihan Kose Cihangir, Kay-Ulrich Scholl, Fabian Oboril, Gereon Hinz, Alois Knoll

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.AI

Comments Paper accepted at CVPR SAIAD Workshop

Improved context-sensitive transformer model for inland vessel trajectory prediction

Kathrin Donandt, Karim Böttger, Dirk Söffker

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.LG

Journal ref Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, pp. 5903-5908

Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor

Chuankai Xu, Dongming Zhao, Bo Wang, Hanwen Xing

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.CL

Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing

Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.LG

Journal ref International Conference on Machine Learning (ICML), 2024

Is machine learning good or bad for the natural sciences?

David W. Hogg, Soledad Villar

专题命中 幻觉与事实性 ：trustworthy(abstract)；分类 cs.LG

Comments A Position Paper accepted for publication in the 2024 International Conference on Machine Learning (ICML)

Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property

Yu Chen, Edoardo Patelli, Zhen Yang, Adolphus Lye

专题命中 幻觉与事实性 ：safety(abstract)；分类 cs.LG

Comments 8 pages, submitted to REC 2024 (International Workshop on Reliable Engineering Computing)

AI-Assisted Detector Design for the EIC (AID(2)E)

M. Diefenthaler, C. Fanelli, L. O. Gerlach, W. Guan, T. Horn, A. Jentsch, M. Lin, K. Nagai, H. Nayak, C. Pecar, K. Suresh, A. Vossen, T. Wang, T. Wenaus

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.AI

Comments 11 pages, 4 figures, AI4EIC 2023 proceeding

Continual Deep Reinforcement Learning for Decentralized Satellite Routing

Federico Lozano-Cuadra, Beatriz Soret, Israel Leyva-Mayorga, Petar Popovski

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.LG

Comments 30 pages, 11 figures

Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks

Felipe Giraldo-Grueso, Andrey A. Popov, Renato Zanetti

专题命中 幻觉与事实性 ：alignment(abstract)；分类 cs.LG

Comments Accepted version, Journal of Aerospace Information Systems

Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation

Ruixin Yang, Dheeraj Rajagopal, Shirley Anugrah Hayati, Bin Hu, Dongyeop Kang

专题命中 幻觉与事实性 ：RLHF(abstract)；分类 cs.CL

Comments Accepted at ICLR 2024 Workshop on Reliable and Responsible Foundation Models