In the domain of text-to-image generative models, the inadvertent propagation
of biases inherent in training datasets poses significant ethical challenges,
particularly in the generation of socially sensitive content. This paper
introduces EquiPrompt, a novel method employing Chain of Thought (CoT)
reasoning to reduce biases in text-to-image generative models. EquiPrompt uses
iterative bootstrapping and bias-aware exemplar selection to balance creativity
and ethical responsibility. It integrates iterative reasoning refinement with
controlled evaluation techniques, addressing zero-shot CoT issues in sensitive
contexts. Experiments on several generation tasks show EquiPrompt effectively
lowers bias while maintaining generative quality, advancing ethical AI and
socially responsible creative processes.Code will be publically available.

采用 CoT 推理的 EquiPrompt 是一种新颖的方法，用于减少文本到图像生成模型中的偏见，并通过迭代推理改进和受控评估技术解决敏感环境中的零样本 CoT 问题，实验表明 EquiPrompt 有效降低偏见同时保持生成质量，推动道德人工智能和负责任的创造过程。

EquiPrompt: 在思维链中通过迭代引导改进传播模型的偏差

EquiPrompt: Debiasing Diffusion Models via Iterative Bootstrapping in  Chain of Thoughts

Code provides a general syntactic structure to build complex programs and
perform precise computations when paired with a code interpreter -- we
hypothesize that language models (LMs) can leverage code-writing to improve
Chain of Thought reasoning not only for logic and arithmetic tasks, but also
for linguistic ones (and in particular, those that are a mix of both). For
example, consider prompting an LM to write code that counts the number of times
it detects sarcasm in an essay: the LM may struggle to write an implementation
for "detect_sarcasm(string)" that can be executed by the interpreter (handling
the edge cases would be insurmountable). However, LMs may still produce a valid
solution if they are used not only to write the code, but also to selectively
"emulate" the interpreter by generating the expected output of
"detect_sarcasm(string)" and other lines of code (e.g., that the interpreter
could not compile). In this work, we propose Chain of Code (CoT), a simple yet
surprisingly effective extension that improves LM code-driven reasoning. The
key idea is to encourage LMs to format linguistic sub-tasks in a program as
flexible pseudocode that the compiler can explicitly catch undefined behaviors
and hand off to simulate with an LM (as an "LMulator"). Experiments demonstrate
that Chain of Code outperforms Chain of Thought and other baselines across a
variety of benchmarks; on BIG-Bench Hard, Chain of Code achieves 84%, a gain of
12% over Chain of Thought. CoT scales well with large and small models alike,
and broadens the scope of reasoning questions that LMs can correctly answer by
"thinking in code". Project webpage: this https URL

我们提出了 Chain of Code (CoT) 这一简单但非常有效的扩展方法，通过鼓励语言模型将语言子任务格式化为编程中的伪代码，以期改进语言模型驱动的推理，并通过 “思考代码” 的方式扩大语言模型能够正确回答的推理问题范围。