Deep Natural Language Feature Learning for Interpretable Prediction
Felipe Urrutia Cristian Buc Valentin Barriere
We propose a general method to break down a main complex task into a set of
intermediary easier sub-tasks, which are formulated in natural language as
binary questions related to the final target task. Our method allows for
representing each example by a vector consisting of the answers to these
questions. We call this representation Natural Language Learned Features
(NLLF). NLLF is generated by a small transformer language model (e.g., BERT)
that has been trained in a Natural Language Inference (NLI) fashion, using weak
labels automatically obtained from a Large Language Model (LLM). We show that
the LLM normally struggles for the main task using in-context learning, but can
handle these easiest subtasks and produce useful weak labels to train a BERT.
The NLI-like training of the BERT allows for tackling zero-shot inference with
any binary question, and not necessarily the ones seen during the training. We
show that this NLLF vector not only helps to reach better performances by
enhancing any classifier, but that it can be used as input of an
easy-to-interpret machine learning model like a decision tree. This decision
tree is interpretable but also reaches high performances, surpassing those of a
pre-trained transformer in some cases.We have successfully applied this method
to two completely different tasks: detecting incoherence in students' answers
to open-ended mathematics exam questions, and screening abstracts for a
systematic literature review of scientific papers on climate change and
agroecology.