The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources

• URL: http://arxiv.org/abs/2406.16746v3
• Date: Tue, 3 Sep 2024 23:03:41 GMT
• Title: The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
• Authors: Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini,
• Abstract summary: Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet.
• Score: 100.23208165760114
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet: a growing collection of 250+ tools and resources spanning text, vision, and speech modalities. We draw on a large body of prior work to survey resources (e.g. software, documentation, frameworks, guides, and practical tools) that support informed data selection, processing, and understanding, precise and limitation-aware artifact documentation, efficient model training, advance awareness of the environmental impact from training, careful model evaluation of capabilities, risks, and claims, as well as responsible model release, licensing and deployment practices. We hope this curated collection of resources helps guide more responsible development. The process of curating this list, enabled us to review the AI development ecosystem, revealing what tools are critically missing, misused, or over-used in existing practices. We find that (i) tools for data sourcing, model evaluation, and monitoring are critically under-serving ethical and real-world needs, (ii) evaluations for model safety, capabilities, and environmental impact all lack reproducibility and transparency, (iii) text and particularly English-centric analyses continue to dominate over multilingual and multi-modal analyses, and (iv) evaluation of systems, rather than just models, is needed so that capabilities and impact are assessed in context.

Related papers

• Deploying Large Language Models With Retrieval Augmented Generation [0.21485350418225244]
  Retrieval Augmented Generation has emerged as a key approach for integrating knowledge from data sources outside of the large language model's training set. We present insights from the development and field-testing of a pilot project that integrates LLMs with RAG for information retrieval.
  arXiv  Detail & Related papers  (2024-11-07T22:11:51Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Reasoning and Tools for Human-Level Forecasting [0.4261908132550109]
  We present Reasoning and Tools for Forecasting (RTF), a framework of reasoning-and-acting (ReAct) agents that can retrieve updated information and run numerical simulation with equipped tools. We evaluate our model with questions from competitive forecasting platforms and demonstrate that our method is competitive with and can outperform human predictions.
  arXiv  Detail & Related papers  (2024-08-21T23:42:06Z)
• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
  Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Large foundation models, such as large language models, have revolutionized various natural language processing tasks. This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
  arXiv  Detail & Related papers  (2024-03-18T17:57:09Z)
• Training and Serving System of Foundation Models: A Comprehensive Survey [32.0115390377174]
  This paper extensively explores the methods employed in training and serving foundation models from various perspectives. It provides a detailed categorization of these state-of-the-art methods, including finer aspects such as network, computing, and storage.
  arXiv  Detail & Related papers  (2024-01-05T05:27:15Z)
• INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models [39.46610170563634]
  INSTRUCTEVAL is a more comprehensive evaluation suite designed specifically for instruction-tuned large language models. We take a holistic approach to analyze various factors affecting model performance, including the pretraining foundation, instruction-tuning data, and training methods. Our findings reveal that the quality of instruction data is the most crucial factor in scaling model performance.
  arXiv  Detail & Related papers  (2023-06-07T20:12:29Z)
• Tool Learning with Foundation Models [158.8640687353623]
  With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field.
  arXiv  Detail & Related papers  (2023-04-17T15:16:10Z)
• On the Opportunities and Risks of Foundation Models [256.61956234436553]
  We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration.
  arXiv  Detail & Related papers  (2021-08-16T17:50:08Z)

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.