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Updated: May 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Ethical-Lens: Curbing malicious usages of open-source text-to-image models.

Yuzhu Cai1,2,3, Sheng Yin1, Yuxi Wei1

  • 1School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Patterns (New York, N.Y.)
|April 4, 2025
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Summary

Ethical-Lens is a new framework for aligning open-source text-to-image models with societal values. It refines prompts and outputs to reduce toxicity and bias without altering the models themselves.

Keywords:
AI safetylarge language modelstext-to-image modelsvalue alignment

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Ethics in AI

Background:

  • Text-to-image models like Midjourney and DALL·E 3 offer powerful content creation capabilities.
  • The open-source nature of some models raises ethical concerns regarding misuse for generating harmful or biased content.
  • Existing solutions often require complex internal model modifications.

Purpose of the Study:

  • To introduce Ethical-Lens, a novel framework for aligning open-source text-to-image models with societal values.
  • To enable value-aligned usage without internal model revision.
  • To address toxicity and bias in AI-generated imagery.

Main Methods:

  • The Ethical-Lens framework refines user commands (prompts) and model outputs.
  • It employs systematic evaluation metrics, including GPT4-V, HEIM, and FairFace scores, to assess alignment.
  • The framework operates externally, without modifying the core text-to-image models.

Main Results:

  • Ethical-Lens significantly enhances the value alignment of open-source text-to-image models.
  • Alignment capabilities were found to be comparable or superior to commercial models like DALL·E 3.
  • The quality of generated images is preserved during the alignment process.

Conclusions:

  • Ethical-Lens offers an effective solution for promoting responsible use of open-source text-to-image technology.
  • The framework facilitates the sustainable development and societal integration of AI image generation tools.
  • Value alignment can be achieved without compromising the performance or utility of open-source models.