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LEVERAGING LLMS FOR CAUSAL REASONING: WHY KNOWLEDGE AND ALGORITHMS ARE KEY

Anthony Alcaraz

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6 days ago

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Causal reasoning — the capacity to understand cause-effect relationships and
make inferences about interventions — is fundamental to intelligence. It
underpins decision making by enabling anticipation of consequences.

This skill was long presumed exclusive to humans, requiring years of experience
to develop sophisticated causal models relating diverse real-world concepts.

However, with artificial intelligence now verging on matching certain human
abilities, there is intense focus on replicating causal cognition — a hallmark
of advanced generalized intelligence.

Could AI systems reason about cause and effect given their lack of a lived
experience in the physical world?

Exciting indications of causal abilities have recently emerged from large
language models, trained via self-supervision on textual data alone.

Prompted with events described in natural language text, these systems exhibit
human-like judgments on assessing causality between statement pairs with high
accuracy.

Some models can even determine necessary or sufficient causes with competence
rivaling untrained humans.

These advances offer a promising glimpse into the future where AI assistants
advise professionals by weighing complex causal implications of potential
decisions, social policies consider AI-generated impact assessments before
implementation, and personal agents tailor recommendations to individual
contexts and preferences using personalized causal models.

However, fully realizing this vision requires confronting the limitations of
pure language-model-based approaches.

Truly reliable and versatile real-world causal reasoning demands tightly
integrating multiple modalities — the fluid reasoning capacity supplied by
language models, fused with both structured world knowledge and algorithmic
logic for robust causal intelligence greater than the sum of its parts.

This article explains why combining language models with knowledge graphs and
specialized algorithms is essential for scalable, practical AI causal reasoning
that can tackle ambiguity, understand context, reason dynamically, and
ultimately enhance human decision making with machine-driven causal wisdom.

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WRITTEN BY ANTHONY ALCARAZ

31K Followers
·Writer for

GoPenAI

Chief AI Officer & Architect : Builder of Neuro-Symbolic AI Systems @Fribl
enhanced GenAI for HR https://www.linkedin.com/in/anthony-alcaraz-b80763155/

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