Search Results for author:
Found 5 papers, 1 papers with code
Human-Centered Evaluation of Explanations
The NLP community are increasingly interested in providing explanations for NLP models to help people make sense of model behavior and potentially improve human interaction with models.
Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation
To explore how humans can best leverage LLMs for writing and how interacting with these models affects feelings of ownership and trust in the writing process, we compared common human-AI interaction types (e. g., guiding system, selecting from system outputs, post-editing outputs) in the context of LLM-assisted news headline generation.
An Exploration of Post-Editing Effectiveness in Text Summarization
Automatic summarization methods are efficient but can suffer from low quality.
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions.
Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments.
