Inclusive language

Clash of Company Needs? Large Language Models (LLM) vs. Inclusive Language

Delve into the world of Large Language Models: Balancing cutting-edge efficiency with the imperative for inclusivity and bias-free communication.

In today's rapidly digitizing world, technological innovations are consistently pushing the boundaries of what's possible. Large Language Models (LLMs) like ChatGPT have emerged as breakthrough tools in the domain of natural language processing, offering unprecedented levels of efficiency and productivity. However, as with all technological marvels, they come with their own set of challenges. One of the most pertinent of these challenges is the balance between harnessing the power of these models and ensuring the language they generate is inclusive and free from bias. In this post, we delve deep into the world of LLMs, exploring their mechanisms, their inherent biases, and the critical importance of fostering inclusivity in the digital language landscape.


LLMs for Productivity

Before diving in, let's explore the architecture of LLMs and pinpoint their inherent shortcomings. What are Large Language Models (LLMs)? Large language models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.

What are Large Language Models (LLMs)?

Large Language Models (LLMs) are advanced machine-learning models designed to process, understand, and generate human-like text. Trained on vast amounts of textual data, LLMs can perform tasks ranging from answering questions and writing essays to coding or translating languages.

How are LLMs trained?

  • LLMs are trained on vast amounts of text, predominantly from the internet.
  • These texts come with their set of biases as they are produced by humans.
  • The training data used to refine these models can come from past materials, carrying with it past cultural and societal norms.
  • The output generated by an LLM is based on patterns seen in the data. It predicts the next word or phrase based on its understanding of what usually follows a given set of words.


Is ChatGPT a large language model?

Yes, ChatGPT is a large language model developed by OpenAI, and one of the most popular ones at the moment. ChatGPT is designed to understand and generate human-like text based on the input it receives. Other notable LLMs include BERT and T5 by Google, as well as GROVER by AllenAI.


Are there any biases in LLMs, and how can they impact outputs?

What are the limitations or challenges of LLMs? Let's have a look at why there might be biases in Large Language Models and check out some examples.

  • Black Box Dilemma: Without explicit details, it's unclear what specific datasets an LLM has been trained on, leading to potential cultural and geographical biases.
  • Bias of data labelers: These models can unintentionally reinforce and propagate existing societal biases.
  • Catching Up with Linguistic Trends: Linguistic developments, especially ones tied to social movements, can emerge rapidly. LLMs may lag in recognizing and appropriately using these new linguistic norms.
  • Risk of Reinforcing Stereotypes: Due to their reliance on statistical patterns, LLMs might perpetuate misleading or harmful stereotypes, which can manifest in outputs.

Check out this conversation with ChatGPT (Thanks go to Hadas Kotek, PhDSuzanne Wertheim, Ph.D. and Ruchika Tulshyan for making us aware of these experiments).

Bildschirmfoto 2023-09-17 um 13.02.40

This conversation shows that there seems to be an underlying bias, possibly resulting from prevalent stereotypes in the training data where "doctor" might often be associated with male pronouns and "nurse" with female pronouns.

I re-did this experiment with ChatGPT4. Though I got a slightly different result in my conversation and ChatGPT4 recognized that in the second phrase that the doctor was meant, ChatGPT4 still fell into the bias traps in the other sentence.

I then confronted ChatGPT with its bias. That's what ChatGPT has to say about it:

chatGPT conversation

Inclusive Language for Company Culture

Risks of Biased Language

Using non-inclusive language can deter talent from diverse backgrounds, create toxicity and fluctuation due to neglect of linguistic developments, miss out on customers with diverse backgrounds and Generation Z, and even lead to lawsuits or partnership cancellations.

Screenshot 2023-10-31 at 14.04.37







The Dilemma of Company Needs: Productivity vs. Culture? 

Benefits of LLMs

The benefits of LLMs are obvious and really useful in daily business. The most important ones are:

  • Speed: Quick content creation.
  • Flexible: Versatile in generating various text styles.
  • Comprehensiveness: An expansive knowledge base.

Benefits of Inclusive Language

Inclusive language fosters an environment where individuals feel recognized and cherished, enhancing workplace cohesion and improving customer interactions.

  • People & Culture: Enhances employee loyalty.
  • Marketing & Communications: Strengthens employer brand appeal.
  • C-Level: Prevent s**tstorms against executives, manage risk in reputation or partnership loss
  • Recruiting: Ensure the attractiveness of the employer brand and help to recruit diversity.

Witty has given me valuable insights regarding the phrasing of job postings. Special attention is given to the best possible approach to GenY, but even more so to GenZ.

Witty's Distinct Approach Compared to LLMs

While Large Language Models like ChatGPT rely on vast datasets and can sometimes produce outputs that are not entirely predictable, Witty stands apart with its unique characteristics. Witty is explainable, ensuring that its processes and decision-making are transparent. It's deterministic, which means it provides consistent results given the same input. Unlike some LLMs that might "hallucinate" or generate content that wasn’t explicitly in the training data, Witty is steadfast in its accuracy. Furthermore, the foundation of Witty is constructed from content that has been meticulously reviewed by experts, guaranteeing a high level of reliability and trustworthiness in its responses.

Witty LLMs such as ChatGPT
Explainable & transparent Intransparent, limited explainability via prompt
Deterministic, provides consistent results Can exhibit variability in outputs
Tailored for sensitivity and inclusivity in language General-purpose language generation
Designed to avoid biases and stereotypes Potential to replicate existing biases in data
Language outputs are curated for equity and diversity Outputs are a reflection of varied internet text
Regular updates to reflect contemporary language shifts Updates dependent on retraining cycles
Context-aware adjustments promoting inclusiveness Contextual understanding without specific inclusivity focus
Expert-reviewed content to ensure appropriateness Large-scale data with minimal human review per item
Purpose-built for diverse and inclusive communication Versatile in function but not specialized in inclusivity



Bridging the Gap: Combining LLM with AI whisperer

Utilizing an LLM whisperer like Witty can help businesses get the best of both worlds: the speed and efficiency of an LLM and the care and nuance of Inclusive Language. By fine-tuning LLMs with insights from Witty, we can have AI-driven content that respects and represents everyone.

Witty logo - heart - ChatGPT logo

Examples of Inclusive and Non-Inclusive Language in Communication

Non-Inclusive Example

Hey guys. The fall event is a Q&A with John, the “god” of employee experience.

Inclusive Version

Hey friends. The fall event is a Q&A with John, the “creative mind” of employee experience.

Examples of Inclusive and Non-Inclusive Language in Recruiting

Non-Inclusive Example

Courage, determination, dedication, and competitive drive will make you win.

Inclusive Version

Curiosity, focus, dedication, and an enterprising spirit will foster your development.


While LLMs like ChatGPT offer powerful tools for productivity, businesses must be vigilant in ensuring that the language used promotes an inclusive culture. With the right strategies and tools, such as Witty, companies can harness the power of LLMs while championing inclusivity.

Nadia Fischer

CEO & Co-founder Witty Works | Speaker on bias in language and business | Expert business development, sales, product strategy | Evangelist for Diversity, Equity, Inclusion, and Belonging

Elements Image

Subscribe to our mailing

Stay in the loop with carefully crafted articles about inclusive language and tips to improve diversity & inclusion in the workplace.

Latest Articles

Image GenAI: Uproar over diverse images in Gemini questionable

Image GenAI: Uproar over diverse images in Gemini questionable

Google's Gemini faced backlash for diverse AI-generated images, including people of color as US founding fathers, sparking debate over hist...

Ethics Board 2024 Update

Ethics Board 2024 Update

As we kick off the new year, I am excited to share the progress made by the Witty Works ethics board throughout 2023.

Clash of Company Needs? Large Language Models (LLM) vs. Inclusive Language

Clash of Company Needs? Large Language Models (LLM) vs. Inclusive Language

Delve into the world of Large Language Models: Balancing cutting-edge efficiency with the imperative for inclusivity and bias-free communic...