27 citations · 27 across the 3 of their papers we have counts for
3 papers
cs.AI2026
The Illusion of AI Expertise Under Uncertainty: A Probabilistic Paradigm to Interpret Results Under Ground Truth Variation
Aparna Elangovan, Lei Xu, Mahsa Elyasi +8
Benchmarking the capabilities of AI systems, including Large Language Models (LLMs) and Vision Models, typically ignores the impact of uncertainty in the underlying ground truth an…
cs.HC2024
Beyond correlation: The Impact of Human Uncertainty in Measuring the Effectiveness of Automatic Evaluation and LLM-as-a-Judge
Aparna Elangovan, Lei Xu, Jongwoo Ko +4
The effectiveness of automatic evaluation of generative models is typically measured by comparing the labels generated via automation with labels by humans using correlation metric…
cs.CL2024★ 27 cited
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models
Aparna Elangovan, Ling Liu, Lei Xu +2
In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon insights from discipline…