22 citations · 27 across the 16 of their papers we have counts for
18 papers
Flexible Routing via Uncertainty Decomposition
Charlotte Peale, Siddartha Devic, Parikshit Gopalan +2
A key strategy for balancing performance and cost in modern machine learning systems is to dynamically route queries to either a low-cost model or a more expensive oracle (such as…
Are LLM Decisions Faithful to Verbal Confidence?
Jiawei Wang, Yanfei Zhou, Siddartha Devic +1
Large Language Models (LLMs) can produce surprisingly sophisticated estimates of their own uncertainty. However, it remains unclear to what extent this expressed confidence is tied…
An External Fairness Evaluation of LinkedIn Talent Search
Tina Behzad, Siddartha Devic, Vatsal Sharan +2
We conduct an independent, third-party audit for bias of LinkedIn's Talent Search ranking system, focusing on potential ranking bias across two attributes: gender and race. To do s…
Trace Length is a Simple Uncertainty Signal in Reasoning Models
Siddartha Devic, Charlotte Peale, Arwen Bradley +3
Uncertainty quantification for LLMs is a key research direction towards addressing hallucination and other issues that limit their reliable deployment. In this work, we show that r…
Auditability and the Landscape of Distance to Multicalibration
Nathan Derhake, Siddartha Devic, Dutch Hansen +2
Calibration is a critical property for establishing the trustworthiness of predictors that provide uncertainty estimates. Multicalibration is a strengthening of calibration which r…
From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered
Siddartha Devic, Tejas Srinivasan, Jesse Thomason +2
Large Language Models (LLMs) are increasingly assisting users in the real world, yet their reliability remains a concern. Uncertainty quantification (UQ) has been heralded as a too…