Evaluating the Robustness of Depth Image Super-Resolution Models

Wang, Dengxiang; Yan, Jiebin; Liu, Xuelin; Zuo, Yifan

doi:10.1109/mmsp55362.2022.9949286

Cited by 1 publication

(1 citation statement)

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“…This approach restricts the model's applicability to limited scenarios and presents difficulties in adapting them to different domains. Though there are initiatives to develop a more generalized entity resolution model, the performance of these broader models remains imperfect [9].…”

Section: Introductionmentioning

confidence: 99%

BoostER: Leveraging Large Language Models for Enhancing Entity Resolution

Li,

Hao

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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Entity resolution, which involves identifying and merging records that refer to the same real-world entity, is a crucial task in areas like Web data integration. This importance is underscored by the presence of numerous duplicated and multi-version data resources on the Web. However, achieving high-quality entity resolution typically demands significant effort. The advent of Large Language Models (LLMs) like GPT-4 has demonstrated advanced linguistic capabilities, which can be a new paradigm for this task. In this paper, we propose a demonstration system named BoostER that examines the possibility of leveraging LLMs in the entity resolution process, revealing advantages in both easy deployment and low cost. Our approach optimally selects a set of matching questions and poses them to LLMs for verification, then refines the distribution of entity resolution results with the response of LLMs. This offers promising prospects to achieve a high-quality entity resolution result for real-world applications, especially to individuals or small companies without the need for extensive model training or significant financial investment.

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Section: Introductionmentioning

confidence: 99%