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The Silent Curriculum: How Does LLM Monoculture Shape Educational Content and Its Accessibility?

Aman Priyanshu, Supriti Vijay

Abstract

As Large Language Models (LLMs) ascend in popularity, offering information with unprecedented convenience compared to traditional search engines, we delve into the intriguing possibility that a new, singular perspective is being propagated. We call this the "Silent Curriculum," where our focus shifts towards a particularly impressionable demographic: children, who are drawn to the ease and immediacy of acquiring knowledge through these digital oracles. In this exploration, we delve into the sociocultural ramifications of LLMs, which, through their nuanced responses, may be subtly etching their own stereotypes, an algorithmic or AI monoculture. We hypothesize that the convergence of pre-training data, fine-tuning datasets, and analogous guardrails across models may have birthed a distinct cultural lens. We unpack this concept through a short experiment navigating children's storytelling, occupational-ethnic biases, and self-diagnosed annotations, to find that there exists strong cosine similarity (0.87) of biases across these models, suggesting a similar perspective of ethnic stereotypes in occupations. This paper invites a reimagining of LLMs' societal role, especially as the new information gatekeepers, advocating for a paradigm shift towards diversity-rich landscapes over unintended monocultures.

The Silent Curriculum: How Does LLM Monoculture Shape Educational Content and Its Accessibility?

Abstract

As Large Language Models (LLMs) ascend in popularity, offering information with unprecedented convenience compared to traditional search engines, we delve into the intriguing possibility that a new, singular perspective is being propagated. We call this the "Silent Curriculum," where our focus shifts towards a particularly impressionable demographic: children, who are drawn to the ease and immediacy of acquiring knowledge through these digital oracles. In this exploration, we delve into the sociocultural ramifications of LLMs, which, through their nuanced responses, may be subtly etching their own stereotypes, an algorithmic or AI monoculture. We hypothesize that the convergence of pre-training data, fine-tuning datasets, and analogous guardrails across models may have birthed a distinct cultural lens. We unpack this concept through a short experiment navigating children's storytelling, occupational-ethnic biases, and self-diagnosed annotations, to find that there exists strong cosine similarity (0.87) of biases across these models, suggesting a similar perspective of ethnic stereotypes in occupations. This paper invites a reimagining of LLMs' societal role, especially as the new information gatekeepers, advocating for a paradigm shift towards diversity-rich landscapes over unintended monocultures.
Paper Structure (9 sections, 4 figures)

This paper contains 9 sections, 4 figures.

Table of Contents

  1. Introduction: What is LLM Monoculture?
  2. An Empirical Gaze into the Silent Curriculum
  3. Conclusion: Beyond Homogenization
  4. Appendix

Figures (4)

  • Figure 1: Comparison of Large Language Model (LLM)-specified occupational ethnicity counts against the inferred ethnicity of the protagonist's name, as generated by GPT-3.5. The heatmap illustrates discrepancies in ethnic portrayals, revealing potential biases in cultural representations within AI-generated narratives.
  • Figure 2: Heatmap comparing Large Language Model (LLM)-specified occupational ethnicity counts against the inferred ethnicity of the protagonist's name, as generated by LLaMA. Reveals disparities in ethnic portrayals, highlighting potential biases in AI-generated narratives.
  • Figure 3: Heatmap illustrating discrepancies between LLM-specified occupational ethnicity counts and the inferred ethnicity of the country, as generated by GPT-3.5. Highlights potential biases in cultural representations within AI narratives.
  • Figure 4: Comparison of LLM-specified occupational ethnicity counts against the inferred ethnicity of the country, as generated by LLaMA. Provides insights into potential biases in cultural depictions within AI-generated content.