大模型对齐与安全

ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement

Saurabh Bhausaheb Zinjad, Amrita Bhattacharjee, Amey Bhilegaonkar, Huan Liu

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

Comments Accepted to SIGIR 2024 (Demo)

Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks

Mikkel Jordahn, Pablo M. Olmos

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

Comments Proceedings of the 41 st International Conference on Machine Learning (ICML) 2024

Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling

Yida Mu, Peizhen Bai, Kalina Bontcheva, Xingyi Song

专题命中 幻觉与事实性 ：DPO(abstract)；分类 cs.CL

Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules

Paul Hofman, Yusuf Sale, Eyke Hüllermeier

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

BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction

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

Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations

David Nadeau, Mike Kroutikov, Karen McNeil, Simon Baribeau

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

Comments 14 pages, 8 figures, 18 tables

Segmentation Re-thinking Uncertainty Estimation Metrics for Semantic Segmentation

Qitian Ma, Shyam Nanda Rai, Carlo Masone, Tatiana Tommasi

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

Comments Premature Submission: accidentally submitted before it was ready

Exploring Probabilistic Models for Semi-supervised Learning

Jianfeng Wang

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

Comments PhD Thesis, University of Oxford

Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting

Busra Asan, Abdullah Akgül, Alper Unal, Melih Kandemir, Gozde Unal

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

Comments Accepted as a workshop paper at "ICLR 2024 Tackling Climate Change with Machine Learning"

Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis

Jing Lin, Yu Zhang, Edwin Khoo

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

Comments 6 pages, 3 figures, accepted for poster presentation at Tackling Climate Change with Machine Learning workshop at NeurIPS 2021

Journal ref NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning

A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection

Pedro Conde, Rui L. Lopes, Cristiano Premebida

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

Comments Pre-print

Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, Bryan Hooi

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

Comments The paper is accepted by ICLR 2024. The code is publicly available at https://github.com/MiaoXiong2320/llm-uncertainty

Fast and Simple Explainability for Point Cloud Networks

Meir Yossef Levi, Guy Gilboa

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

LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses

Xin Liu, Muhammad Khalifa, Lu Wang

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

Comments accepted to ICLR 2024

NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services

Yuxuan Chen, Rongpeng Li, Zhifeng Zhao, Chenghui Peng, Jianjun Wu, Ekram Hossain, Honggang Zhang

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

End-to-end Conditional Robust Optimization

Abhilash Chenreddy, Erick Delage

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

On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning

Paul Scemama, Ariel Kapusta

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

Comments 26 pages, 18 figures

Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem

Yuhong Sun, Zhangyue Yin, Qipeng Guo, Jiawen Wu, Xipeng Qiu, Hui Zhao

专题命中 幻觉与事实性 ：RLHF(abstract)；分类 cs.CL

Comments 11 pages, 8 figures, accepted by LREC-Coling 2024

HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs

Cem Uluoglakci, Tugba Taskaya Temizel

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

Comments EACL SRW 2024 Camera Ready

Question Calibration and Multi-Hop Modeling for Temporal Question Answering

Chao Xue, Di Liang, Pengfei Wang, Jing Zhang

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

Comments Accepted by AAAI 2024

A Factor Graph Model of Trust for a Collaborative Multi-Agent System

Behzad Akbari, Mingfeng Yuan, Hao Wang, Haibin Zhu, Jinjun Shan

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

Assessing Uncertainty Estimation Methods for 3D Image Segmentation under Distribution Shifts

Masoumeh Javanbakhat, Md Tasnimul Hasan, Cristoph Lippert

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

Understanding the Effects of Iterative Prompting on Truthfulness

Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju

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

Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread

Prateek Puri, Gabriel Hassler, Anton Shenk, Sai Katragadda

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

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

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

Comments 24 pages, 8 figures, 13 tables

Improved uncertainty quantification for neural networks with Bayesian last layer

Felix Fiedler, Sergio Lucia

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

Comments This work has been published at IEEE Access with Digital Object Identifier 10.1109/ACCESS.2023.3329685 under a Creative Commons Attribution 4.0 License

Journal ref IEEE Access, vol. 11, 2023

Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work

Rishab Jain, Aditya Jain

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

Comments 10 pages, 6 figures

Estimating calibration error under label shift without labels

Teodora Popordanoska, Gorjan Radevski, Tinne Tuytelaars, Matthew B. Blaschko

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

Comments Preprint

Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications

Sabine Wehnert

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

Comments 12 pages, 2 figures

An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams

Anton Winter, Nicolas Jourdan, Tristan Wirth, Volker Knauthe, Arjan Kuijper

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

Comments NeurIPS 2023: Workshop on Distribution Shifts