March 29, 2024, 4:43 a.m. | Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu

cs.LG updates on

arXiv:2403.19631v1 Announce Type: cross
Abstract: Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge updates, leading to potentially outdated or inaccurate responses. This problem becomes even more challenging when dealing with multi-hop questions since they require LLMs to update and integrate multiple knowledge pieces relevant to the questions. To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework tailored for multi-hop question answering. RAE first retrieves edited facts and then …

abstract arxiv cs.lg editing knowledge language language models large language large language models llms multiple question question answering questions real-time responses retrieval struggle tasks type update updates

Data Scientist (m/f/x/d)

@ Symanto Research GmbH & Co. KG | Spain, Germany

Data Analyst


EY GDS Internship Program - Junior Data Visualization Engineer (June - July 2024)

@ EY | Wrocław, DS, PL, 50-086

Staff Data Scientist

@ ServiceTitan | INT Armenia Yerevan

Master thesis on deterministic AI inference on-board Telecom Satellites

@ Airbus | Taufkirchen / Ottobrunn

Lead Data Scientist

@ Picket | Seattle, WA