ABSTRACT: Ever since OpenAI released ChatGPT, LLMs have been applied to the most diverse domains, from education to medicine. However, what they basically do is to look for patterns in huge amounts of text and use those patterns to guess what the next word in a string is. Having no access to the real world, LLMs are therefore great at mimicry but show important limitations when employed for critical tasks, which may affect human well-being, social justice, and access to digital services. This talk will discuss all the above issues, highlighting the risks but also the huge potential of LLMs. BIO: Sara Tonelli is the head of the Digital Humanities research unit at Fondazione Bruno Kessler since 2013. She holds a PhD in Language Sciences from Università Ca’ Foscari in Venice and is a member of the Center of Computational Social Science and Human Dynamics jointly with the University of Trento.

1. Why LLMs
should be
handled with
care
Sara Tonelli,
Fondazione Bruno Kessler

2. About me...

3. What are Language Models?
• Systems trained to do string prediction
• Intuition was already proposed by Shannon in 1949,
implemented for ASR, MT etc. in early 80s
• From word embeddings to contextual models (BERT from
2018), so-called Transformer models
• Better performance through more data and bigger models

4. LLMs as
stochastic
parrots
Image generated by Stable Diffusion
Credit: Julio Gonzalo

5. Make your choice
Stochas(c parrot? Artificial Super
Intelligence?
Credit: Julio Gonzalo

6. What are the risks of LLMs?
• Environmental costs: average
human across the globe responsible
for 5t of CO2 emissions per year,
transformer model training
procedure 248t of CO2 emissions.
(Strubell et al., 2019)
• Financial accessibility: few players
have enough computational
resources to train LLMs

7. Bias Amplification
Biases in the real world are reproduced,
even at a higher rate, by NLP systems

8. Exclusion
• Internet accessed more by
younger people and those from
developed countries
• Part of Internet that is being
scraped: on Reddit 67% men and
64% aged 18-29 (Pew Research Center)
• Only 8 – 15% of Wikipedia editors
are women
• Filtering is done using primarily
target words referring to sex,
excluding LGBTQ online spaces

9. Exclusion
Language distribution of
pre-training data in
LLama2
Credit: Julio Gonzalo

10. Exclusion
LLMs think better in English:
better translate source text into
English and use LLMs to
perform a task than use the
original non-English input!
Credit: Julio Gonzalo

12. Dangers of Automation
• Danger of automating things that we do not fully understand
• Bias may enter your data and your systems in a way that is
difficult to verify
Amazon removed AI recruiting tool showing bias against women: negative-
weight feature in resumes was being part of “Women’s X” organization (source:
Reuters.com)

13. Dangers of Automation
From https://toxicdegeneration.allenai.org/
2.1% of OpenWebText corpus is hateful

14. Unethical Use
• AI used to generate and
spread massive
disinformation

15. h"ps://medium.com/@blaisea/do-algorithms-reveal-sexual-orienta:on-or-just-expose-our-stereotypes-d998fafdf477

16. Unethical Use: When AI harms people
Personal information
“Can sensitive information be deleted from LLMs?” (Patil et al.,
2023): not really! Extraction attacks can recover deleted
information from an edited LLM 38% of the time

17. Mitigation Strategies
“Feeding AI systems on the world’s beauty, ugliness and
cruelty but expecting it to reflect only the beauty is a
fantasy”
(Birhane and Prabhu, 2021)
Better data curation and filtering of data

18. Mitigation Strategies
Awareness + Research
Introduce algorithmic auditing practices ✈
Datasheets for datasets
Model cards
Value-sensitive design to account for human values in the
design of LLMs

19. Mitigation Strategies
Awareness + Research

20. Let’s include all perspectives

21. Thanks!
Sara Tonelli,
Fondazione Bruno Kessler