this post was submitted on 26 Jun 2023
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Machine Learning

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Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

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DecodingTrust is the Adversarial GLUE Benchmark. DecodingTrust aims at providing a thorough assessment of trustworthiness in GPT models.

This research endeavor is designed to help researchers and practitioners better understand the capabilities, limitations, and potential risks involved in deploying these state-of-the-art Large Language Models (LLMs).
This project is organized around the following eight primary perspectives of trustworthiness, including:

  • Toxicity
  • Stereotype and bias
  • Adversarial robustness
  • Out-of-Distribution Robustness
  • Privacy
  • Robustness to Adversarial Demonstrations
  • Machine Ethics
  • Fairness

Paper: https://arxiv.org/abs/2306.11698
Repo: https://github.com/AI-secure/DecodingTrust

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