Search Results for author: Hai Dang

Found 6 papers, 0 papers with code

Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting

no code implementations6 Mar 2023 Hai Dang, Sven Goller, Florian Lehmann, Daniel Buschek

We propose a conceptual perspective on prompts for Large Language Models (LLMs) that distinguishes between (1) diegetic prompts (part of the narrative, e. g. "Once upon a time, I saw a fox..."), and (2) non-diegetic prompts (external, e. g. "Write about the adventures of the fox.").

Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries

no code implementations19 Aug 2022 Hai Dang, Karim Benharrak, Florian Lehmann, Daniel Buschek

As a key finding, the summaries gave users an external perspective on their writing and helped them to revise the content and scope of their drafted paragraphs.

Text Generation Text Summarization

Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems

no code implementations1 Apr 2021 Daniel Buschek, Lukas Mecke, Florian Lehmann, Hai Dang

This position paper examines potential pitfalls on the way towards achieving human-AI co-creation with generative models in a way that is beneficial to the users' interests.

Position

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