大模型对齐与安全 - arXivDaily 专题

2411.03497 2024-11-07 cs.CL 57%

Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models

Zizhang Chen, Peizhao Li, Xiaomeng Dong, Pengyu Hong

专题命中幻觉与事实性：safety(abstract)；分类 cs.CL

2312.08248 2024-11-06 cs.AI 57%

A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective

Huan Yan, Yong Li

专题命中幻觉与事实性：safety(abstract)；分类 cs.AI

Comments Revised version submitted to ACM CSUR

2410.14155 2024-11-04 cs.CL 57%

Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models

Wei Jie Yeo, Ranjan Satapathy, Erik Cambria

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

Comments Under review

2406.01563 2024-11-01 cs.CL 57%

LoFiT: Localized Fine-tuning on LLM Representations

Fangcong Yin, Xi Ye, Greg Durrett

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

Comments NeurIPS 2024 Camera Ready

2410.04241 2024-10-30 cs.CL 57%

Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations

Sagi Shaier, Ari Kobren, Philip Ogren

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

Comments Accepted to EMNLP 2024

2410.20199 2024-10-29 cs.AI 57%

Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models

Mohammad Beigi, Sijia Wang, Ying Shen, Zihao Lin, Adithya Kulkarni, Jianfeng He, Feng Chen, Ming Jin, Jin-Hee Cho, Dawei Zhou, Chang-Tien Lu, Lifu Huang

专题命中幻觉与事实性：safety(abstract)；分类 cs.AI

2402.04957 2024-10-24 cs.CL 57%

Reconfidencing LLMs from the Grouping Loss Perspective

Lihu Chen, Alexandre Perez-Lebel, Fabian M. Suchanek, Gaël Varoquaux

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

Comments EMNLP 2024 Findings

2410.16397 2024-10-23 cs.AI cs.ET 57%

Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight

Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Wafa M. Sadri, Carsten Hartmann, Tobias Hecking, J. Nathan Kutz

专题命中幻觉与事实性：safety(abstract)；分类 cs.AI

Comments 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 October 2024

2410.15926 2024-10-22 cs.CV cs.CL 57%

Mitigating Object Hallucination via Concentric Causal Attention

Yun Xing, Yiheng Li, Ivan Laptev, Shijian Lu

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

Comments To appear at NeurIPS 2024. Code is available at https://github.com/xing0047/cca-llava

2403.19381 2024-10-22 stat.ML cs.LG 57%

On Uncertainty Quantification for Near-Bayes Optimal Algorithms

Ziyu Wang, Chris Holmes

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2410.14010 2024-10-21 cs.LG 57%

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information

Ömer Faruk Akgül, Rajgopal Kannan, Viktor Prasanna

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2405.07726 2024-10-17 cs.CL 57%

Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing

Letian Peng, Jingbo Shang

专题命中幻觉与事实性：DPO(abstract)；分类 cs.CL

Comments NeurIPS2024

2406.16562 2024-10-11 cs.CV cs.CL 57%

EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models

Zhiyu Tan, Xiaomeng Yang, Luozheng Qin, Mengping Yang, Cheng Zhang, Hao Li

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

Comments Project page: https://sais-fuxi.github.io/projects/evalalign/

2402.11035 2024-10-07 cs.CL cs.IR 57%

Dense Passage Retrieval: Is it Retrieving?

Benjamin Reichman, Larry Heck

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

2410.01064 2024-10-03 cs.AI 57%

Truth or Deceit? A Bayesian Decoding Game Enhances Consistency and Reliability

Weitong Zhang, Chengqi Zang, Bernhard Kainz

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

2405.00871 2024-09-30 eess.SY cs.LG cs.SY 57%

Learning to Boost the Performance of Stable Nonlinear Systems

Luca Furieri, Clara Lucía Galimberti, Giancarlo Ferrari-Trecate

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Journal ref IEEE Open Journal of Control Systems (Volume: 3), 2024

2405.16908 2024-09-27 cs.CL 57%

Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

Gal Yona, Roee Aharoni, Mor Geva

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

Comments To appear in EMNLP 2024 (main conference)

2409.17190 2024-09-27 cs.CR cs.AI 57%

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

2402.09801 2024-09-24 cs.CL cs.CV 57%

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

2401.10768 2024-09-24 cs.CL 57%

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)

2409.13045 2024-09-23 cs.CV cs.LG 57%

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

2307.09494 2024-09-20 cs.NI cs.LG 57%

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

2402.17124 2024-09-10 cs.CL 57%

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

2010.13892 2024-09-04 q-fin.ST cs.LG 57%

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

2406.15264 2024-08-26 cs.IR cs.CL 57%

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

2408.06220 2024-08-13 cs.LG cs.CE 57%

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

2404.07503 2024-08-13 cs.CL 57%

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

2407.21535 2024-08-01 cs.LG 57%

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

2407.21417 2024-08-01 cs.CL 57%

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

2406.19768 2024-07-02 cs.LG 57%

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

1. 幻觉与事实性 1756 篇

Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models

A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective

Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models

LoFiT: Localized Fine-tuning on LLM Representations

Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations

Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models

Reconfidencing LLMs from the Grouping Loss Perspective

Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight

Mitigating Object Hallucination via Concentric Causal Attention

On Uncertainty Quantification for Near-Bayes Optimal Algorithms

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information

Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing

EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models

Dense Passage Retrieval: Is it Retrieving?

Truth or Deceit? A Bayesian Decoding Game Enhances Consistency and Reliability

Learning to Boost the Performance of Stable Nonlinear Systems

Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

Enhancing Guardrails for Safe and Secure Healthcare AI

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

Knowledge Verification to Nip Hallucination in the Bud

TACE: Tumor-Aware Counterfactual Explanations

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

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

Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction

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

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

Best Practices and Lessons Learned on Synthetic Data

Probabilistic Scoring Lists for Interpretable Machine Learning

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

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