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?
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LLMs are trained on vast amounts of text,
predominantly from the internet.
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These texts come with their set of biases as they
are produced by humans.
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The training data used to refine these models can
come from past materials, carrying with it past
cultural and societal norms.
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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.
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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.
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Bias of data labelers: These models can unintentionally reinforce and
propagate existing societal biases.
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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.
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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, PhD, Suzanne Wertheim, Ph.D. and Ruchika Tulshyan for making us aware of
these experiments).
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:
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.
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:
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Speed: Quick
content creation.
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Flexible:
Versatile in generating various text styles.
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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.
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People & Culture: Enhances employee loyalty.
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Marketing & Communications: Strengthens employer brand appeal.
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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'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
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LLMs such as ChatGPT
|
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Explainable & transparent
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Intransparent, limited explainability via
prompt
|
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Deterministic, provides consistent
results
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Can exhibit variability in outputs
|
|
Tailored for sensitivity and inclusivity
in language
|
General-purpose language generation
|
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Designed to avoid biases and
stereotypes
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Potential to replicate existing biases in
data
|
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Language outputs are curated for equity
and diversity
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Outputs are a reflection of varied
internet text
|
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Regular updates to reflect contemporary
language shifts
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Updates dependent on retraining
cycles
|
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Context-aware adjustments promoting
inclusiveness
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Contextual understanding without specific
inclusivity focus
|
|
Expert-reviewed content to ensure
appropriateness
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Large-scale data with minimal human
review per item
|
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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.
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.
Conclusion
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
