8 citations · 8 across the 3 of their papers we have counts for
5 papers
SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models
Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee +4
When does a large language model (LLM) know what it does not know? Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM…
The Consistency Hypothesis in Uncertainty Quantification for Large Language Models
Quan Xiao, Debarun Bhattacharjya, Balaji Ganesan +5
Estimating the confidence of large language model (LLM) outputs is essential for real-world applications requiring high user trust. Black-box uncertainty quantification (UQ) method…
Business Entity Matching with Siamese Graph Convolutional Networks
Evgeny Krivosheev, Mattia Atzeni, Katsiaryna Mirylenka +3
Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automatio…
Siamese Graph Neural Networks for Data Integration
Evgeny Krivosheev, Mattia Atzeni, Katsiaryna Mirylenka +2
Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automatio…
Fast Record Linkage for Company Entities
Thomas Gschwind, Christoph Miksovic, Julian Minder +2
Record linkage is an essential part of nearly all real-world systems that consume structured and unstructured data coming from different sources. Typically no common key is availab…