  Keyword mnemonics are memorable explanations that link new terms to simpler
keywords. Prior works generate mnemonics for students, but they do not guide
models toward mnemonics students prefer and aid learning. We build SMART, a
mnemonic generator trained on feedback from real students learning new terms.
To train SMART, we first fine-tune LLaMA-2 on a curated set of user-written
mnemonics. We then use LLM alignment to enhance SMART: we deploy mnemonics
generated by SMART in a flashcard app to find preferences on mnemonics students
favor. We gather 2684 preferences from 45 students across two types: expressed
(inferred from ratings) and observed (inferred from student learning), yielding
three key findings. First, expressed and observed preferences disagree; what
students think is helpful does not fully capture what is truly helpful. Second,
Bayesian models can synthesize complementary data from multiple preference
types into a single effectiveness signal. SMART is tuned via Direct Preference
Optimization on this signal, which we show resolves ties and missing labels in
the typical method of pairwise comparisons, augmenting data for LLM output
quality gains. Third, mnemonic experts assess SMART as matching GPT-4, at much
lower deployment costs, showing the utility of capturing diverse student
feedback to align LLMs in education.
