The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models (2502.11266v1)

Abstract: Language is far more than a communication tool. A wealth of information - including but not limited to the identities, psychological states, and social contexts of its users - can be gleaned through linguistic markers, and such insights are routinely leveraged across diverse fields ranging from product development and marketing to healthcare. In four studies utilizing experimental and observational methods, we demonstrate that the widespread adoption of LLMs as writing assistants is linked to notable declines in linguistic diversity and may interfere with the societal and psychological insights language provides. We show that while the core content of texts is retained when LLMs polish and rewrite texts, not only do they homogenize writing styles, but they also alter stylistic elements in a way that selectively amplifies certain dominant characteristics or biases while suppressing others - emphasizing conformity over individuality. By varying LLMs, prompts, classifiers, and contexts, we show that these trends are robust and consistent. Our findings highlight a wide array of risks associated with linguistic homogenization, including compromised diagnostic processes and personalization efforts, the exacerbation of existing divides and barriers to equity in settings like personnel selection where language plays a critical role in assessing candidates' qualifications, communication skills, and cultural fit, and the undermining of efforts for cultural preservation.

The Shrinking Landscape of Linguistic Diversity in the Age of LLMs

The paper "The Shrinking Landscape of Linguistic Diversity in the Age of LLMs" addresses a critical aspect of linguistic evolution, namely the influence of LLMs on linguistic diversity. Authored by a multidisciplinary team from the University of Southern California, this paper examines how the proliferation of LLMs such as ChatGPT and Gemini leads to a homogenization of language and the potential consequences of this trend.

Summary of Findings

The research employs both experimental and observational methods across four distinct studies to evaluate the influence of LLMs on linguistic diversity. By adopting a multifaceted approach, the authors comprehensively investigate not only the extent of linguistic homogenization but also its implications on societal and individual levels.

1. Study 1 focuses on textual data from Reddit, Patch News, and arXiv, illustrating a decline in linguistic variance post-LLM adoption. The analysis reveals that texts increasingly align with standardized writing styles, an effect attributed to the probabilistic nature of LLMs, which favor dominant linguistic patterns.
2. Study 2 uses controlled prompts to elicit text revisions from LLMs, evidencing a consistent reduction in the variance of linguistic complexity while maintaining core semantic content. This experimentally substantiates the observational findings from Study 1, indicating a trend towards linguistic convergence.
3. Study 3 evaluates the impact on the ability to derive personal or demographic insights from text, showing that homogenization obscures meaningful linguistic markers essential for such analyses.
4. Study 4 further explores how LLMs alter established lexical-cognitive associations, highlighting shifts that align linguistic output with specific demographic attributes.

Theoretical and Practical Implications

The paper underscores several key implications of linguistic homogenization:

• Psychological and Societal Insights: The diminished variability in language usage can compromise the identification of individual or group-specific linguistic markers, relevant for psychological profiling and cultural analyses. This reduction in diversity risks impairing our understanding of cognitive and social dynamics across diverse populations.
• Equity Concerns: In professional sectors such as hiring, the homogenization of writing styles may introduce bias, favoring candidates whose texts align with standardized norms, thus exacerbating existing inequalities.
• Cultural Heritage and Preservation: The use of LLMs risks eroding linguistic diversity, which is fundamental to cultural identity. As LLMs amplify dominant language patterns, there is a potential threat to minority languages and dialects, which are crucial for cultural preservation.
• Cognitive Flexibility: The convergence towards homogenous language might limit cognitive diversity and creativity by reducing the pool of linguistic structures available for thought experimentation and innovation.

Future Directions

The research raises critical questions about the trajectories of linguistic evolution and the role of LLMs in shaping future communication modes. Addressing these concerns will require:

• Enhanced Model Design: Developing LLMs with mechanisms to recognize and preserve linguistic diversity, thereby accommodating a broader range of expressions.
• Ethical Considerations: Policymakers and researchers must engage in dialogue to mitigate biases and ensure equitable representation in AI-driven linguistic models.
• Cross-disciplinary Research: Further studies should explore the intersection of cognitive science, linguistics, and AI to understand and enhance the interface between human and machine communication.

In conclusion, while LLMs undeniably offer significant advancements in processing and generating text, this paper cautions against uncritical adoption, urging for a balanced approach that safeguards linguistic diversity in the digital age.