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Do Massively Pretrained Language Models Make Better Storytellers?
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Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks.However, the strength of these models as Natural Language Generators is less clear.Though anecdotal evidence suggests that these models generate better quality text, there has been no detailed study characterizing their generation abilities.In this work, we compare the performance of an extensively pretrained model, OpenAI GPT2-117 (Radford et al., 2019), to a state-of-the-art neural story generation model (Fan et al., 2018).By evaluating the generated text across a wide variety of automatic metrics, we characterize the ways in which pretrained models do, and do not, make better storytellers.We find that although GPT2-117 conditions more strongly on context, is more sensitive to ordering of events, and uses more unusual words, it is just as likely to produce repetitive and under-diverse text when using likelihood-maximizing decoding algorithms.
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Fiction-Writing Mode: An Effective Control for Human-Machine Collaborative Writing
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CreativeBot: a Creative Storyteller Agent Developed by Leveraging Pre-trained Language Models
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Metadata
- Authors
- See, A., Pappu, A., Saxena, R., Yerukola, A., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
- Published
- 2019
- Pages
- 843-861
- OpenAlex
- W2975003218
- Cited by
- 139
- References
- 31
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 11 · 2023: 27 · 2022: 18 · 2021: 43 · 2020: 30 · 2019: 2
- FWCI
- 8.89
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Computational Natural Language Learning (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Tencent
- Defense Advanced Research Projects Agency
- Army Research Office (W911NF-15-1-, W911NF, W911NF-15)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- story generation, pre-trained language models, GPT-2, natural language generation, text generation quality, automatic evaluation metrics, event ordering, lexical diversity, decoding algorithms, likelihood maximization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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