大模型对齐与安全

Medical Reasoning in LLMs: An In-Depth Analysis of DeepSeek R1

Birger Moell, Fredrik Sand Aronsson, Sanian Akbar

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

Latent Lexical Projection in Large Language Models: A Novel Approach to Implicit Representation Refinement

Ziad Shaker, Brendan Ashdown, Hugo Fitzalan, Alistair Heathcote, Jocasta Huntington

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

Comments arXiv admin note: This paper has been withdrawn by arXiv due to disputed and unverifiable authorship

GAN-enhanced Simulation-driven DNN Testing in Absence of Ground Truth

Mohammed Attaoui, Fabrizio Pastore

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

Comments 15 pages, 8 figures, 13 tables

Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models

Seungeun Oh, Jinhyuk Kim, Jihong Park, Seung-Woo Ko, Tony Q. S. Quek, Seong-Lyun Kim

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

Comments 7 pages, 6 figures; to be presented at IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) 2025

Investigating Human-Aligned Large Language Model Uncertainty

Kyle Moore, Jesse Roberts, Daryl Watson, Pamela Wisniewski

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

DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models

YiQiu Guo, Yuchen Yang, Zhe Chen, Pingjie Wang, Yusheng Liao, Ya Zhang, Yanfeng Wang, Yu Wang

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

Can Structured Data Reduce Epistemic Uncertainty?

Shriram M S, Sushmitha S, Gayathri K S, Shahina A

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

Comments Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)

Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows

Frederic Gmeiner, Nicolai Marquardt, Michael Bentley, Hugo Romat, Michel Pahud, David Brown, Asta Roseway, Nikolas Martelaro, Kenneth Holstein, Ken Hinckley, Nathalie Riche

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

Comments 31 pages, 30 figures, 3 tables. To appear in the Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems, Yokohama, Japan

Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality

Hang Wang, Qiaoyi Fang, Junshan Zhang

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

Comments CPAL 2025

Random-Set Neural Networks (RS-NN)

Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang, Keivan Shariatmadar, Fabio Cuzzolin

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

Comments Published as a conference paper at the Thirteenth International Conference on Learning Representations (ICLR 2025)

Uncertainty Quantification and Decomposition for LLM-based Recommendation

Wonbin Kweon, Sanghwan Jang, SeongKu Kang, Hwanjo Yu

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

Comments WWW 2025

Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology

Abhinav Prakash Gahlot, Rafael Orozco, Felix J. Herrmann

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

From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical Predictions

Ubaid Azam, Imran Razzak, Shelly Vishwakarma, Hakim Hacid, Dell Zhang, Shoaib Jameel

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

Trustworthiness in Stochastic Systems: Towards Opening the Black Box

Jennifer Chien, David Danks

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

Comments 22 pages, 2 figures

Approximate Message Passing for Bayesian Neural Networks

Romeo Sommerfeld, Christian Helms, Ralf Herbrich

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

Comments for code see https://github.com/christian-helms/mpbnns.git

Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization

Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin

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

Comments Submitted to ARR October 2024

Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction

Paul Melki, Lionel Bombrun, Boubacar Diallo, Jérôme Dias, Jean-Pierre da Costa

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

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles

Yuxi Xia, Pedro Henrique Luz de Araujo, Klim Zaporojets, Benjamin Roth

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

Comments 24 pages, 11 figures, 8 tables

EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models

Andrés Villa, Juan León Alcázar, Motasem Alfarra, Vladimir Araujo, Alvaro Soto, Bernard Ghanem

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

Comments 12 pages, 4 figures, 8 tables

Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation

Derong Xu, Xinhang Li, Ziheng Zhang, Zhenxi Lin, Zhihong Zhu, Zhi Zheng, Xian Wu, Xiangyu Zhao, Tong Xu, Enhong Chen

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

Comments Accepted by AAAI'2025

U-GIFT: Uncertainty-Guided Firewall for Toxic Speech in Few-Shot Scenario

Jiaxin Song, Xinyu Wang, Yihao Wang, Yifan Tang, Ru Zhang, Jianyi Liu, Gongshen Liu

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

Comments 16 pages, 6 figures and 10 tables. Comments are welcome

Extract Free Dense Misalignment from CLIP

JeongYeon Nam, Jinbae Im, Wonjae Kim, Taeho Kil

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

Comments 16 pages, 14 figures, AAAI 2025

Factuality or Fiction? Benchmarking Modern LLMs on Ambiguous QA with Citations

Maya Patel, Aditi Anand

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

Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring

Markus Borg

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

Comments Accepted for publication in the Proc. of the 2nd Workshop on Integrated Development Environments, 2025

A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models

Gongbo Zhang, Zihan Xu, Qiao Jin, Fangyi Chen, Yilu Fang, Yi Liu, Justin F. Rousseau, Ziyang Xu, Zhiyong Lu, Chunhua Weng, Yifan Peng

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

See Where You Read with Eye Gaze Tracking and Large Language Model

Sikai Yang, Gang Yan, Wan Du

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

Comments 9 pages

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion

Jiuhai Chen, Jianwei Yang, Haiping Wu, Dianqi Li, Jianfeng Gao, Tianyi Zhou, Bin Xiao

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

Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models

John Fischer, Marko Orescanin, Justin Loomis, Patrick McClure

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

Comments 22 pages, 9 figures

Journal ref IEEE Access (2024)

Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry

Mostafa Cherif, Tobías I. Liaudat, Jonathan Kern, Christophe Kervazo, Jérôme Bobin

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

Comments 14 pages, 7 figures. Accepted at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2024

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems

Benjamin Kolicic, Alberto Caron, Chris Hicks, Vasilios Mavroudis

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