Authors: Hao Chen, Bhiksha Raj, Xing Xie, Jindong Wang

Published on: February 02, 2024

Impact Score: 8.27

Arxiv code: Arxiv:2402.01909

Summary

• What is new: Identification and analysis of Catastrophic Inheritance in Large Foundation Models (LFMs), a concept describing how biases and limitations from pre-training data affect downstream applications.
• Why this is important: LFMs inherit weaknesses and limitations from their pre-training data, leading to issues like bias, lack of generalization, deteriorated performance, security vulnerabilities, privacy leaks, and value misalignment in downstream tasks.
• What the research proposes: Introduction of the UIM framework, aimed at Understanding, Interpreting, and Mitigating the effects of Catastrophic Inheritance in LFMs, bringing together machine learning and social sciences for better AI development.
• Results: The paper outlines the scope of Catastrophic Inheritance, potential impacts, and the preliminary steps for the UIM framework to address these issues, aiming for more responsible AI deployment.

Technical Details

Technological frameworks used: UIM (Understand, Interpret, Mitigate)

Models used: Large Foundation Models (LFMs)

Data used: Biased large-scale pre-training data

Potential Impact

AI development and deployment sectors, especially companies relying on LFMs for downstream applications such as content moderation, recommendation systems, and automated decision-making processes.

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