2 citations · 4 across the 4 of their papers we have counts for
4 papers
Black-box Uncertainty Quantification Method for LLM-as-a-Judge
Nico Wagner, Michael Desmond, Rahul Nair +6
LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty…
Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences
Zahra Ashktorab, Michael Desmond, Qian Pan +7
Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes ti…
Emerging Reliance Behaviors in Human-AI Content Grounded Data Generation: The Role of Cognitive Forcing Functions and Hallucinations
Zahra Ashktorab, Qian Pan, Werner Geyer +5
We investigate the impact of hallucinations and Cognitive Forcing Functions in human-AI collaborative content-grounded data generation, focusing on the use of Large Language Models…
Human-Centered Design Recommendations for LLM-as-a-Judge
Qian Pan, Zahra Ashktorab, Michael Desmond +5
Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-qua…