Financial markets are one of the few machine-learning settings where feedback is fast, mistakes are expensive, and deployment is constrained by execution and regulation. Yet most existing tutorials still treat finance either as an NLP benchmark or as a classical forecasting problem. This tutorial takes a different view. We show how recent progress in generative AI becomes genuinely useful in markets when it helps model participant behavior, ground reasoning in live documents and order-flow data, and support research and execution workflows that can survive contact with production. The material therefore moves in three steps: behavioral and market foundations; grounded LLM systems, including RAG, critique models, and agentic workflows; and responsible deployment, including corrected backtesting, execution, risk controls, and governance. Two live demonstrations and three optional Colab notebooks connect the lecture material to implementation.
Z. Iklassov, Hachem Madmoun, J. Duhot et al.· Proceedings of the 32nd ACM...· 0 citations
Large embedding models improve retrieval quality, but serving large encoders online is expensive. We study whether a compact retriever can learn teacher ranking behavior from score vectors without access to teacher hidden states. The student trains on rows built from ground-truth positives and negative candidates produced by our data generation pipeline; we evaluate student-teacher hard-negative mining separately as an extension. We use a row-centered score-vector objective, a memory-efficient implementation of uniform all-pairs PairMSE loss. On a fixed eight-task evaluation panel, our distillation protocol recovers up to 50% of the base-to-teacher gap. The distilled 0.6B student is 4.7 times faster for query encoding and 9.7 times faster for document encoding than sequential online teacher fusion. External-transfer performance after distillation remains mixed, so our evidence supports compression of teacher rankings under matched retrieval protocols.
K. Dubovikov, Martin Takác, S. Lahlou· 0 citations