LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic
Understanding Tasks

LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks

19 September 2024

Malsha Ashani Mahawatta Dona

Beatriz Cabrero-Daniel

Yinan Yu

Christian Berger

Papers citing "LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks"

7 / 7 papers shown

Title
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models S.M. Towhidul Islam Tonmoy S. M. M. Zaman Vinija Jain Anku Rani Vipula Rawte Aman Chadha Amitava Das HILM 85 198 0 02 Jan 2024
Evaluating Large Language Models: A Comprehensive Survey Zishan Guo Renren Jin Chuang Liu Yufei Huang Dan Shi ... Linhao Yu Yan Liu Jiaxuan Li Bojian Xiong Deyi Xiong ELM LM&MA 51 187 0 30 Oct 2023
CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering Md. Rony Christian Suess Sinchana Ramakanth Bhat Viju Sudhi Julia Schneider Maximilian Vogel Roman Teucher Ken E. Friedl S. Sahoo 62 10 0 14 Oct 2023
Ragas: Automated Evaluation of Retrieval Augmented Generation ES Shahul Jithin James Luis Espinosa-Anke Steven Schockaert 108 188 0 26 Sep 2023
Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models Mohamed S. Elaraby Mengyin Lu Jacob Dunn Xueying Zhang Yu Wang Shizhu Liu Pingchuan Tian Yuping Wang Yuxuan Wang HILM 53 27 0 22 Aug 2023
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 581 41,736 0 28 May 2020
Scalability in Perception for Autonomous Driving: Waymo Open Dataset Pei Sun Henrik Kretzschmar Xerxes Dotiwalla Aurelien Chouard Vijaysai Patnaik ... Shuyang Cheng Yu Zhang Jonathon Shlens Zhifeng Chen Dragomir Anguelov 85 2,871 0 10 Dec 2019