L a SQ u E : Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning (2023)

Abstract: A hallmark of human intelligence is the ability to learn new concepts purely from language. Several recent approaches have explored training machine learning models via natural language supervision. However, these approaches fall short in leveraging linguistic quantifiers (such as ‘always’ or ‘rarely’) and mimicking humans in compositionally learning complex tasks. Here, we present LaSQuE, a method that can learn zero-shot classifiers from language explanations by using three new strategies - (1) modeling the semantics of linguistic quantifiers in explanations (including exploiting ordinal strength relationships, such as ‘always’ > ‘likely’), (2) aggregating information from multiple explanations using an attention-based mechanism, and (3) model training via curriculum learning. With these strategies, LaSQuE outperforms prior work, showing an absolute gain of up to 7% in generalizing to unseen real-world classification tasks.

## introduction learning from language (also 'conversational machine learning') is a new paradigm of machine learning where machines are taught tasks through natural language supervision in the form of explanations and instructions @xcite @xcite . language explanations of concepts have been explored for training classification models in few-shot and zero-shot settings @xcite @xcite @xcite @xcite . however, current approaches fall short in fully leveraging supervision available in language explanations and using learning strategies that humans routinely employ in learning new tasks. first, most odor stalksurfaceabove-ring gillcolor pungent smooth white a mushroom is usually poisonous if it has pungent odor. mushrooms with a silk stalk surface are poisonous. ## lasque in this section, we present our method, lasque, and provide detailed descriptions and empirical support for the different learning strategies that are part of lasque-(1) modeling quantifier semantics, (2) using attention for aggregation across explanations, and (3) curriculum learning. ## conclusion we have presented effective and generalizable strategies to learn classifiers from language explanations. while our results are promising, our analysis also highlights several open challenges in learn-