[On Demand] Advancing Multilingual Causal Reasoning for LLMs
Watch on-demand and explore how well large language models (LLMs) handle causal reasoning across languages—and where they still fall short.
Causal reasoning is fundamental to AI’s ability to interpret the world, yet training data limitations can impact LLM performance. In this panel, Welo Data shares its methodology for evaluating causal reasoning in AI, why this matters for future AI advancements and key takeaways from the latest research.
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Advancing Multilingual Causal Reasoning for LLMs
Topics Include
- Why causal reasoning is essential for AI advancement.
- A preview of our findings and expectations about their impact on the industry.
- What’s next in multilingual and cross-lingual research.
Moderator
Tally Callahan, Solutions Architect, Welo Data
Speakers
- Dr. Larry Carin, Welo Data advisor, Duke University
- Olesia Khrapunova, AI/ML Engineer, Welocalize
- Konstantinos Karageorgos, AI/ML Engineering Lead, Welocalize