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Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing

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arxiv 2210.13669 v1 pith:EU6ZSULV submitted 2022-10-25 cs.CL

classification cs.CL
keywords instructionslanguagellmswritecopoetpoetrysystemwriting
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent work in training large language models (LLMs) to follow natural language instructions has opened up exciting opportunities for natural language interface design. Building on the prior success of LLMs in the realm of computer-assisted creativity, we aim to study if LLMs can improve the quality of user-generated content through collaboration. We present CoPoet, a collaborative poetry writing system. In contrast to auto-completing a user's text, CoPoet is controlled by user instructions that specify the attributes of the desired text, such as Write a sentence about `love' or Write a sentence ending in `fly'. The core component of our system is a language model fine-tuned on a diverse collection of instructions for poetry writing. Our model is not only competitive with publicly available LLMs trained on instructions (InstructGPT), but is also capable of satisfying unseen compositional instructions. A study with 15 qualified crowdworkers shows that users successfully write poems with CoPoet on diverse topics ranging from Monarchy to Climate change. Further, the collaboratively written poems are preferred by third-party evaluators over those written without the system.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback

    cs.CL 2025-07 conditional novelty 6.0 of 10

    On a new controlled dataset of corrupted short stories, eight LLMs produce mostly correct, specific writing feedback but often fail to identify the biggest writing issue and are poor at deciding when to say a story is...

  2. HACo-Det: A Study Towards Fine-Grained Machine-Generated Text Detection under Human-AI Coauthoring

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Current machine-generated text detectors, especially metric-based ones, perform poorly on word-level detection in coauthored texts, while finetuned DeBERTa achieves strong but imperfect performance.

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