by Chun-Sung Huang, Marcus Gawronsky
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Suggested foundations for text and language models in finance and probability and statistical reasoning. Specialist prerequisites vary by paper.
Understand how text is converted into numerical inputs.
Check whether labels measure the financial construct of interest.
Read the distributions and expectations used in probabilistic arguments.
Interpret expectations after information is observed.
Test whether a text-derived signal survives an honest financial evaluation.
Measure agreement when constructing or checking labels.
Assess uncertainty rather than treating an estimate as a fact.
Move from conditioning and expected values to timed problems with a defensible solution.