大模型对齐与安全

Do LLMs estimate uncertainty well in instruction-following?

Juyeon Heo, Miao Xiong, Christina Heinze-Deml, Jaya Narain

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

DAHRS: Divergence-Aware Hallucination-Remediated SRL Projection

Sangpil Youm, Brodie Mather, Chathuri Jayaweera, Juliana Prada, Bonnie Dorr

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

Comments 15 pages, 6 figures, Accepted to The 29th International Conference on Natural Language & Information Systems (NLDB 2024)

To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts

Yukun Huang, Sanxing Chen, Hongyi Cai, Bhuwan Dhingra

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

Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling

Xinyue Fang, Zhen Huang, Zhiliang Tian, Minghui Fang, Ziyi Pan, Quntian Fang, Zhihua Wen, Hengyue Pan, Dongsheng Li

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

Comments Accepted by AAAI25

An Empirical Analysis of Uncertainty in Large Language Model Evaluations

Qiujie Xie, Qingqiu Li, Zhuohao Yu, Yuejie Zhang, Yue Zhang, Linyi Yang

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

Comments ICLR 2025

Zero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning

Jiaheng Li, Donghe Li, Ye Yang, Huan Xi, Yu Xiao, Li Sun, Dou An, Qingyu Yang

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

When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges

Abdenour Hadid, Tanujit Chakraborty, Daniel Busby

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

Journal ref Expert Systems, 2024, Volume: 41, Issue: 10

Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs

Premtim Sahitaj, Iffat Maab, Junichi Yamagishi, Jawan Kolanowski, Sebastian Möller, Vera Schmitt

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

Probabilistic Artificial Intelligence

Andreas Krause, Jonas Hübotter

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

Agent-Based Uncertainty Awareness Improves Automated Radiology Report Labeling with an Open-Source Large Language Model

Hadas Ben-Atya, Naama Gavrielov, Zvi Badash, Gili Focht, Ruth Cytter-Kuint, Talar Hagopian, Dan Turner, Moti Freiman

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

ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

Romain Hardy, Sung Eun Kim, Du Hyun Ro, Pranav Rajpurkar

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

Comments Accepted to AIMedHealth 10 pages, 5 figures

Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?

Ipek Baris Schlicht, Zhixue Zhao, Burcu Sayin, Lucie Flek, Paolo Rosso

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

Comments 9 pages. Short paper appeared at 47th European Conference on Information Retrieval (ECIR 2025)

Load and Renewable Energy Forecasting Using Deep Learning for Grid Stability

Kamal Sarkar

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

Journal ref Proceedings of How AI can help in Green Energy, 24 Dec 2024, Science City , Kolkata, India

A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word GRNG for AI Uncertainty Estimation

Zephan M. Enciso, Boyang Cheng, Likai Pei, Jianbo Liu, Steven Davis, Michael Niemier, Ningyuan Cao

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

Comments 7 pages, 12 figures

A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation

Steven Landgraf, Rongjun Qin, Markus Ulrich

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

A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval

Shuo Tong, Han Liu, Runyuan Guo, Wenqing Wang, Xueqiong Tian, Lingyun Wei, Lin Zhang, Huayong Wu, Ding Liu, Youmin Zhang

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

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Tianyang Wang, Yunze Wang, Jun Zhou, Benji Peng, Xinyuan Song, Charles Zhang, Xintian Sun, Qian Niu, Junyu Liu, Silin Chen, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Ming Liu, Yichao Zhang, Cheng Fei, Caitlyn Heqi Yin, Lawrence KQ Yan

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

Comments 14 pages

Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models

Yuheng Huang, Jiayang Song, Zhijie Wang, Shengming Zhao, Huaming Chen, Felix Juefei-Xu, Lei Ma

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

Comments Update website, code, and experiments on eight new LLMs. To appear in the IEEE Transactions on Software Engineering (2025)

From Hallucinations to Facts: Enhancing Language Models with Curated Knowledge Graphs

Ratnesh Kumar Joshi, Sagnik Sengupta, Asif Ekbal

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

Comments 14 Pages, 5 Tables, 2 figures

Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI

Houjiang Liu, Anubrata Das, Alexander Boltz, Didi Zhou, Daisy Pinaroc, Matthew Lease, Min Kyung Lee

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

Comments Accepted at CSCW 2024

A Survey of Calibration Process for Black-Box LLMs

Liangru Xie, Hui Liu, Jingying Zeng, Xianfeng Tang, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Qi He

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

Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation

Qinhong Lin, Linna Zhou, Zhongliang Yang, Yuang Cai

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

O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

Zhen Huang, Haoyang Zou, Xuefeng Li, Yixiu Liu, Yuxiang Zheng, Ethan Chern, Shijie Xia, Yiwei Qin, Weizhe Yuan, Pengfei Liu

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

Comments 16 pages

Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space

Xin Qiu, Risto Miikkulainen

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

Comments Accepted to Neurips 2024

Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization

George Chrysostomou, Zhixue Zhao, Miles Williams, Nikolaos Aletras

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

Comments TACL 2024 (Presented at EMNLP 2024)

Journal ref Transactions of the Association for Computational Linguistics (2024) 12: 1163-1181

Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation

Magdalena Wysocka, Oskar Wysocki, Maxime Delmas, Vincent Mutel, Andre Freitas

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

Comments Accepted at the Journal of Biomedical Informatics, Volume 158, October 2024, 104724

Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search

Chenglin Li, Qianglong Chen, Zhi Li, Feng Tao, Yicheng Li, Hao Chen, Fei Yu, Yin Zhang

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

Mind the Uncertainty in Human Disagreement: Evaluating Discrepancies between Model Predictions and Human Responses in VQA

Jian Lan, Diego Frassinelli, Barbara Plank

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

Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance

Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, Nouha Dziri, Dan Jurafsky, Maarten Sap

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

Comments Preprint

AI Enabled Neutron Flux Measurement and Virtual Calibration in Boiling Water Reactors

Anirudh Tunga, Jordan Heim, Michael Mueterthies, Thomas Gruenwald, Jonathan Nistor

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

Journal ref 13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2023)