Best Practices and Lessons Learned on Synthetic Data

• URL: http://arxiv.org/abs/2404.07503v2
• Date: Sat, 10 Aug 2024 20:46:47 GMT
• Title: Best Practices and Lessons Learned on Synthetic Data
• Authors: Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, Andrew M. Dai,
• Abstract summary: The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
• Score: 83.63271573197026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness. We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models.

Related papers

• Little Giants: Synthesizing High-Quality Embedding Data at Scale [71.352883755806]
  We introduce SPEED, a framework that aligns open-source small models to efficiently generate large-scale embedding data. SPEED uses only less than 1/10 of the GPT API calls, outperforming the state-of-the-art embedding model E5_mistral when both are trained solely on their synthetic data.
  arXiv  Detail & Related papers  (2024-10-24T10:47:30Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• When AI Eats Itself: On the Caveats of Data Pollution in the Era of Generative AI [18.641925577551557]
  Generative artificial intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. To minimize training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. Not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimize outcomes.
  arXiv  Detail & Related papers  (2024-05-15T13:50:23Z)
• Synthetic Data in AI: Challenges, Applications, and Ethical Implications [16.01404243695338]
  This report explores the multifaceted aspects of synthetic data. It emphasizes the challenges and potential biases these datasets may harbor. It also critically addresses the ethical considerations and legal implications associated with synthetic datasets.
  arXiv  Detail & Related papers  (2024-01-03T09:03:30Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• The Use of Synthetic Data to Train AI Models: Opportunities and Risks for Sustainable Development [0.6906005491572401]
  This paper investigates the policies governing the creation, utilization, and dissemination of synthetic data. A well crafted synthetic data policy must strike a balance between privacy concerns and the utility of data.
  arXiv  Detail & Related papers  (2023-08-31T23:18:53Z)
• Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data [91.52783572568214]
  Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
  arXiv  Detail & Related papers  (2023-04-07T16:38:40Z)
• Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances [76.34037366117234]
  We introduce a new dataset called Robot Control Gestures (RoCoG-v2) The dataset is composed of both real and synthetic videos from seven gesture classes. We present results using state-of-the-art action recognition and domain adaptation algorithms.
  arXiv  Detail & Related papers  (2023-03-17T23:23:55Z)
• Enabling Synthetic Data adoption in regulated domains [1.9512796489908306]
  The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms. In particular, the sensitive nature of the information in highly regulated scenarios needs to be accounted for. A clever way to bypass such a conundrum relies on Synthetic Data: data obtained from a generative process, learning the real data properties.
  arXiv  Detail & Related papers  (2022-04-13T10:53:54Z)

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.