Trusting Language Models in Education
Jogi Suda Neto Li Deng Thejaswi Raya Reza Shahbazi Nick Liu Adhitya Venkatesh Miral Shah Neeru Khosla Rodrigo Capobianco Guido
Language Models are being widely used in Education. Even though modern deep
learning models achieve very good performance on question-answering tasks,
sometimes they make errors. To avoid misleading students by showing wrong
answers, it is important to calibrate the confidence - that is, the prediction
probability - of these models. In our work, we propose to use an XGBoost on top
of BERT to output the corrected probabilities, using features based on the
attention mechanism. Our hypothesis is that the level of uncertainty contained
in the flow of attention is related to the quality of the model's response
itself.