most citedFacilitating Human-LLM Collaboration through Factuality Scores and Source Attributions

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.HC2025

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…

cs.HC2024★ 3 cited

Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions

Hyo Jin Do, Rachel Ostrand, Justin D. Weisz +5

While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advanc…