Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation

• URL: http://arxiv.org/abs/2305.11317v2
• Date: Sat, 28 Oct 2023 04:13:44 GMT
• Title: Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation
• Authors: Wanrong Zhu, Xinyi Wang, Yujie Lu, Tsu-Jui Fu, Xin Eric Wang, Miguel Eckstein and William Yang Wang
• Abstract summary: We investigate the potential of utilizing large-scale language models, such as GPT-k, to improve the prompt editing process for text-to-image generation. We compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process.
• Score: 114.80518907146792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of text-to-image (T2I) generation has garnered significant attention both within the research community and among everyday users. Despite the advancements of T2I models, a common issue encountered by users is the need for repetitive editing of input prompts in order to receive a satisfactory image, which is time-consuming and labor-intensive. Given the demonstrated text generation power of large-scale language models, such as GPT-k, we investigate the potential of utilizing such models to improve the prompt editing process for T2I generation. We conduct a series of experiments to compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process. We found that GPT-k models focus more on inserting modifiers while humans tend to replace words and phrases, which includes changes to the subject matter. Experimental results show that GPT-k are more effective in adjusting modifiers rather than predicting spontaneous changes in the primary subject matters. Adopting the edit suggested by GPT-k models may reduce the percentage of remaining edits by 20-30%.

Related papers

• Optimal path for Biomedical Text Summarization Using Pointer GPT [21.919661430250798]
  GPT models have a tendency to generate factual errors, lack context, and oversimplify words. To address these limitations, we replaced the attention mechanism in the GPT model with a pointer network. The effectiveness of the Pointer-GPT model was evaluated using the ROUGE score.
  arXiv  Detail & Related papers  (2024-03-22T02:13:23Z)
• (Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection [1.9226023650048942]
  Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. We evaluate their ability to solve two diachronic extensions of the Word-in-Context (WiC) task: TempoWiC and HistoWiC.
  arXiv  Detail & Related papers  (2024-01-25T09:36:58Z)
• Does Correction Remain A Problem For Large Language Models? [63.24433996856764]
  This paper investigates the role of correction in the context of large language models by conducting two experiments. The first experiment focuses on correction as a standalone task, employing few-shot learning techniques with GPT-like models for error correction. The second experiment explores the notion of correction as a preparatory task for other NLP tasks, examining whether large language models can tolerate and perform adequately on texts containing certain levels of noise or errors.
  arXiv  Detail & Related papers  (2023-08-03T14:09:31Z)
• Is ChatGPT Involved in Texts? Measure the Polish Ratio to Detect ChatGPT-Generated Text [48.36706154871577]
  We introduce a novel dataset termed HPPT (ChatGPT-polished academic abstracts) It diverges from extant corpora by comprising pairs of human-written and ChatGPT-polished abstracts instead of purely ChatGPT-generated texts. We also propose the "Polish Ratio" method, an innovative measure of the degree of modification made by ChatGPT compared to the original human-written text.
  arXiv  Detail & Related papers  (2023-07-21T06:38:37Z)
• Stay on topic with Classifier-Free Guidance [57.28934343207042]
  We show that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks.
  arXiv  Detail & Related papers  (2023-06-30T17:07:02Z)
• GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content [27.901155229342375]
  We present a novel approach for detecting ChatGPT-generated vs. human-written text using language models. Our models achieved remarkable results, with an accuracy of over 97% on the test dataset, as evaluated through various metrics.
  arXiv  Detail & Related papers  (2023-05-13T17:12:11Z)
• AugGPT: Leveraging ChatGPT for Text Data Augmentation [59.76140039943385]
  We propose a text data augmentation approach based on ChatGPT (named AugGPT) AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach.
  arXiv  Detail & Related papers  (2023-02-25T06:58:16Z)
• Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification [59.698811329287174]
  We leverage GPT-2 for generating artificial training instances in order to improve classification performance. Our results show that fine-tuning GPT-2 in a handful of label instances leads to consistent classification improvements.
  arXiv  Detail & Related papers  (2021-11-17T12:10:03Z)
• GenAug: Data Augmentation for Finetuning Text Generators [21.96895115572357]
  We propose and evaluate various augmentation methods, including some that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp Reviews. Our experiments demonstrate that insertion of character-level synthetic noise and keyword replacement with hypernyms are effective augmentation methods.
  arXiv  Detail & Related papers  (2020-10-05T05:46:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.