REVIEW 4 cited by
Art or Artifice? Large Language Models and the False Promise of Creativity
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Researchers have argued that large language models (LLMs) exhibit high-quality writing capabilities from blogs to stories. However, evaluating objectively the creativity of a piece of writing is challenging. Inspired by the Torrance Test of Creative Thinking (TTCT), which measures creativity as a process, we use the Consensual Assessment Technique [3] and propose the Torrance Test of Creative Writing (TTCW) to evaluate creativity as a product. TTCW consists of 14 binary tests organized into the original dimensions of Fluency, Flexibility, Originality, and Elaboration. We recruit 10 creative writers and implement a human assessment of 48 stories written either by professional authors or LLMs using TTCW. Our analysis shows that LLM-generated stories pass 3-10X less TTCW tests than stories written by professionals. In addition, we explore the use of LLMs as assessors to automate the TTCW evaluation, revealing that none of the LLMs positively correlate with the expert assessments.
Forward citations
Cited by 4 Pith papers
-
Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback
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...
-
Dynamic Reinforcement Learning for Actors
A reinforcement learning update that adjusts each neuron's input-output sensitivity using TD error can replace external exploration noise and backpropagation through time in small actor-critic tasks.
-
Polymind: Parallel Visual Diagramming with Large Language Models to Support Prewriting Through Microtasks
Polymind introduces parallel, configurable LLM microtasks on a diagramming canvas for prewriting, and a small user study indicates it affords users more control and customization than turn-taking chatbot interaction.
-
Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation
SKD-CAG erases adversarial text triggers from diffusion models by distilling the model's own clean outputs through cross-attention guidance, claiming 100% and 93% removal for pixel and style backdoors.
Discussion (0). Continue with ORCID to comment.