The field of large language models (LLMs) is moving towards a greater emphasis on sociolinguistic awareness and cultural sensitivity. Researchers are recognizing the importance of considering the nuances of language and its impact on user engagement and trust. Studies have shown that LLMs can perpetuate stereotypes and biases if not designed with careful consideration of cultural and linguistic boundaries.

As a result, there is a growing need for LLMs to be tailored to specific linguistic and cultural contexts, rather than relying on a one-size-fits-all approach. This includes developing localized models that take into account regional dialects and sociocultural differences.

Furthermore, researchers are exploring the potential of LLMs to promote social equity and mitigate stigma towards marginalized professions. Interactive fiction frameworks powered by LLMs have been shown to increase empathy and understanding towards stigmatized occupations.

Noteworthy papers in this area include: A study on measuring perceptions and behavioral effects of minoritized anthropomorphic cues in LLMs, which highlights the need for careful consideration of sociolect usage in LLM design. A paper on using LLM-based perspective-taking interactive fiction to reduce stigma towards dirty work, which demonstrates the potential of LLMs to promote social equity. A study on auditing human and AI-crafted personas, which reveals the risks of algorithmic othering and the importance of narrative-aware evaluation metrics.