What’s in the Brief?

Building language models capable of reconstructing counterparty intentions demands more than just advanced algorithms—it requires high-quality, well-curated data.

Financial chat data is anything but straightforward—fragmented messages, jargon, and context shifts are just the beginning. Without a strong data curation process, even the most advanced models can fall short.

Download Our Post-Webinar Brief to learn

• Annotation Frameworks: Providing clarity and structure to extract meaningful insights.

• Data Quality Assurance: The role of expert teams, thorough documentation, and iterative review loops in maintaining consistency and accuracy.

• Schema Adaptability: Why schemas need to evolve with market trends, new formats, and edge cases to stay effective.

• Real-World Solutions: Addressing ambiguity, polysemy, and context shifts in financial chats with innovative approaches.