  This paper describes the work of the UniBuc Archaeology team for CLPsych's
2024 Shared Task, which involved finding evidence within the text supporting
the assigned suicide risk level. Two types of evidence were required:
highlights (extracting relevant spans within the text) and summaries
(aggregating evidence into a synthesis). Our work focuses on evaluating Large
Language Models (LLM) as opposed to an alternative method that is much more
memory and resource efficient. The first approach employs a good old-fashioned
machine learning (GOML) pipeline consisting of a tf-idf vectorizer with a
logistic regression classifier, whose representative features are used to
extract relevant highlights. The second, more resource intensive, uses an LLM
for generating the summaries and is guided by chain-of-thought to provide
sequences of text indicating clinical markers.
