Licon: A Diverse, Controllable and Challenging Linguistic Concept Learning Benchmark (2023)

Abstract: Concept Learning requires learning the definition of a general category from given training examples. Most of the existing methods focus on learning concepts from images. However, the visual information cannot present abstract concepts exactly, which struggles the introduction of novel concepts related to known concepts (e.g., ‘Plant’ \rightarrow ‘Asteroids’). In this paper, inspired by the fact that humans learn most concepts through linguistic description, we introduce Linguistic Concept Learning benchmark (Licon), where concepts in diverse forms (e.g., plain attributes, images, and text) are defined by linguistic descriptions. The difficulty to learn novel concepts can be controlled by the number of attributes or the hierarchical relationships between concepts. The diverse and controllable concepts are used to support challenging evaluation tasks, including concept classification, attribute prediction, and concept relationship recognition. In addition, we design an entailment-based concept learning method (EnC) to model the relationship among concepts. Extensive experiments demonstrate the effectiveness of EnC. The benchmark will be released to the public soon.

## introduction concept learning aims to learn the definition of a general category from given information @xcite . this task formulates the innate human ability of generalization in terms of concepts, i.e., the ability to quickly learn novel concepts from few information based on known concepts. prior work mainly focuses on visual concept learning @xcite @xcite , discovering concepts from the visual information (e.g., images). however, the images can only display concrete information via limited examples, such as colors and ## dataset construction in this section, we describe the collection details of our proposed benchmark, licon. we divided licon into three datasets, namely licon-a (synthetic, plain attribute), licon-i (synthetic, image), and licon-t (real-world, text) according to the different construction methods and data sources. ## dataset analysis in this section, we describe the statistics and data distributions of licon. dataset statistics: ## conclusion in this paper, we introduce licon, a diverse, controllable and challenging benchmark for linguistic concept learning, involving plain attributes, images and text. besides, we develop the enc, an entailment-based model to capture the hierarchical relationships between concepts. extensive experiments demonstrate the effectiveness of enc.