3 citations · 6 across the 10 of their papers we have counts for
4 papers · 1 filter
Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judges
Hyo Jin Do, Zahra Ashktorab, Jasmina Gajcin +5
The LLM-as-a-judge paradigm enables flexible, user-defined evaluation, but its effectiveness is often limited by the scarcity of diverse, representative data for refining criteria.…
Hide or Highlight: Understanding the Impact of Factuality Expression on User Trust
Hyo Jin Do, Werner Geyer
Large language models are known to produce outputs that are plausible but factually incorrect. To prevent people from making erroneous decisions by blindly trusting AI, researchers…
Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
Hyo Jin Do, Rachel Ostrand, Werner Geyer +3
Large language models (LLMs) are susceptible to generating inaccurate or false information, often referred to as "hallucinations" or "confabulations." While several technical advan…
EvalAssist: A Human-Centered Tool for LLM-as-a-Judge
Zahra Ashktorab, Werner Geyer, Michael Desmond +6
With the broad availability of large language models and their ability to generate vast outputs using varied prompts and configurations, determining the best output for a given tas…