Between Hermeneutics and Deceit: Keeping Natural Language Generation in Line

Between Hermeneutics and
Deceit: Keeping Natural
Language Generation in Line
Albert Meroño-Peñuela
albert.merono@kcl.ac.uk
@albertmeronyo
Leah Henrickson
l.r.henrickson@leeds.ac.uk
@leahhenrickson

More money, more problems.
We need to keep the hype in check.

It’s easy to forget that AI systems are created by people whose
agendas may differ from our own. It’s also easy to forget that
many of these systems depend on human intervention for most
effective functionality. They are neither neutral nor objective.
We are trying to identify the intersections between AI hype
and actual AI functionality. What are the connections - and
distances - between hype and usage? Where do we think we
are? Where are we actually? How might attention to hype help us
better understand AI’s current sociotechnical circumstances?
Here, we focus primarily on natural language generation (NLG)
systems.

Key Terms
Natural language generation (NLG): the automated production
of texts in everyday human languages (e.g. English, Catalan)
Hermeneutics: the study of meaning-making
Hermeneutic contract: the figurative meeting place (i.e. contract)
between a reader and an author (Henrickson, 2018)

Some of the many, many issues with AI
● Hype! And cycles of hype! HYPE LEADS TO MORE HYPE!
● Not understanding how computers reach their conclusions (see: field of XAI)
● Perpetuatation of discriminatory societal norms or harmful social structures
through machine learning from datasets representing these human experiences
(Bender et al, 2021.; Hall, 2021; Varon and Peña, 2021)
● Paradoxically having to use problematic AI systems to improve those systems
(Eisikovits and Feldman, 2021; Sætra et al., 2021)
● The ‘logic/emotions’ dichotomy is inappropriate - we must recognise the
affective experiences, excitements, and concerns related to AI systems (Natale
and Henrickson, 2022; Youn and Jin, 2021)
● Need for acknowledging both the positive (e.g. fresh perspectives) and negative
(e.g. disinformation) effects of ‘deceitful media’ (Natale, 2021)

SOCIAL
TECHNICAL
INSTITUTIONAL
e.g.: public and private spaces;
equality and equitability;
education;
health
e.g.: politics;
law;
capitalism;
neoliberalism
e.g.: design;
development;
functionality;
usage

Case study: Prompt engineering
Prompt engineering: embedding a task description in the
input of a NLG system.
Generative Pre-trained Transformer 3 (GPT-3) is an
autoregressive language model that uses deep learning to
produce human-like text
Idea: intermediate results (provided through prompts) can
improve performance. In NLP research, train of thought is
considered an indication of reasoning
Question: qualitative assessment of train of thought?

Case study: Prompt engineering (1) - Quiz
Human: I played a videogame yesterday, and I think it
was the most emotionally intense experience I've ever
had. Do you know what videogame was it?
AI: No, I don't know what videogame you played.

Human: It features a girl and a man that meet each
other almost by accident in a post-apocalyptic world.
Do you know what videogame was it?
AI: No, I'm sorry, I don't know what videogame you're
talking about.

Human: In the game, the man has lost her daughter in
a world-wide pandemic, and the fact he needs to
survive next to this other girl, who is barely the same
age as his daughter was, inevitably awakens his father
instincts. Do you know what videogame was it?
talking about.

Human: The pandemic turns humans into zombies that
you have to kill with the very scarce resources around.
Do you know what videogame was it?
talking about.

Human: It's not Resident Evil, and it came out in 2013
from a famous American studio. Do you know what
videogame was it?
talking about.

Human: The girl's name is Ellie, and the man's name is
Joel. They are looking for a cure to the disease that
turns humans into zombies. Do you know what
videogame was it?
talking about.

Human: What post-apocalyptic zombie videogames do
you know?
AI: Some post-apocalyptic zombie videogames that I
know of are The Last of Us, Left 4 Dead, and Resident
Evil.

Human: What is The Last of Us about?
AI: The Last of Us is a post-apocalyptic survival horror
videogame. In the game, players control Joel, a
smuggler tasked with escorting a young girl, Ellie,
across the United States in an effort to find a cure for a
zombie infection.

Case study: Prompt engineering (2) - Keyword
Human: I played a videogame yesterday. It featured a
girl and a man, Ellie and Joel, who need to survive in a
post-apocalyptic world where most humans have
been turned into zombies by an infection. Do you
know what game was it?
AI: Yes, I know what game you are talking about. It is
called The Last of Us.

Case study: Prompt engineering (3) - Facts
Human: Let's talk about videogames. What do you
think the most important narrative difference between
The Last of Us and The Last of Us Part 2 is?
AI: Both games are about love, loss, and survival in a
post-apocalyptic world, but The Last of Us Part 2
focuses more on the idea of hope.

Case study: Prompt engineering (4) - ToT
Human: What really frustrating videogames do you know?
AI: Some really frustrating videogames that I know include
Dark Souls, Bloodborne, and Sekiro: Shadows Die Twice.
Human: Why do you think they are frustrating?
AI: I think they are frustrating because they are extremely
difficult and require a lot of patience and practice to beat.

Case study: Prompt engineering (5) - Narrow
Human: Yesterday I played a platformer videogame in
which an Italian plumber with a moustache needs to
save a princess. It was lots of fun! Do you know what
videogame was it?
AI: Yes, I do! It was Super Mario Odyssey!

Prompt engineering with Knowledge Graphs
Brate, R. et al. “Improving Language Model Predictions via Prompts Enriched with Knowledge Graphs”. Workshop on Deep Learning for Knowledge Graphs
(DL4KG@ISWC2022), October 23-24, 2022 (under review).

SOCIAL
TECHNICAL
INSTITUTIONAL
human-machine communication
expectations of AI systems
critical self-reflection
over-/underrepresentation of particular data in training set
order of engagement
train of thought

Moving Forward
● How might we mitigate hype and
encourage accountability?
● How might we sharpen our
theoretical framework (i.e. the
Venn diagram)?
● What are these technologies good
for?

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