mlsanityData to 14 Oct 2026

Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation

arXiv 2310.10706 ↗First posted 16 Oct 2023cs.CL cs.AIcited by 14

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Abstract (arXiv)

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. While LLMs alone can generate satisfactory news headlines, on average, human control is needed to fix undesirable model outputs. Of the interaction methods, guiding and selecting model output added the most benefit with the lowest cost (in time and effort). Further, AI assistance did not harm participants' perception of control compared to freeform editing.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 16 Oct 2023.

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26 of 47 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  12. 2023Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Promptingcited by 36
14 more
  1. 2020AI Song Contest: Human-AI Co-Creation in Songwritingcited by 25
  2. 2019Visual Interaction with Deep Learning Models through Collaborative Semantic Inferencecited by 24
  3. 2023Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativitycited by 23
  4. 2019Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learningcited by 19
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  6. 2022Mapping the Design Space of Human-AI Interaction in Text Summarizationcited by 15
  7. 2020Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generationcited by 12
  8. 2021GenNI: Human-AI Collaboration for Data-Backed Text Generationcited by 11
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  14. 2021Hone as You Read: A Practical Type of Interactive Summarizationcited by 4

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