236 citations · 275 across the 8 of their papers we have counts for
8 papers
Dissecting users' needs for search result explanations
Prerna Juneja, Wenjuan Zhang, Alison Marie Smith-Renner +3
There is a growing demand for transparency in search engines to understand how search results are curated and to enhance users' trust. Prior research has introduced search result e…
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task
Neema Kotonya, Saran Krishnasamy, Joel Tetreault +1
This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Langu…
Defining a New NLP Playground
Sha Li, Chi Han, Pengfei Yu +8
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in…
Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation
Zijian Ding, Alison Smith-Renner, Wenjuan Zhang +2
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 hu…
Mapping the Design Space of Human-AI Interaction in Text Summarization
Ruijia Cheng, Alison Smith-Renner, Ke Zhang +2
Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans' roles, exper…
There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
Courtney Napoles, Keisuke Sakaguchi, Joel Tetreault
Current methods for automatically evaluating grammatical error correction (GEC) systems rely on gold-standard references. However, these methods suffer from penalizing grammatical…