SAGE: Structured Attribute Value Generation for Billion-Scale Product
  Catalogs
Athanasios N. Nikolakopoulos Swati Kaul Siva Karthik Gade Bella Dubrov Umit Batur Suleiman Ali Khan
We introduce SAGE; a Generative LLM for inferring attribute values for
products across world-wide e-Commerce catalogs. We introduce a novel
formulation of the attribute-value prediction problem as a Seq2Seq
summarization task, across languages, product types and target attributes. Our
novel modeling approach lifts the restriction of predicting attribute values
within a pre-specified set of choices, as well as, the requirement that the
sought attribute values need to be explicitly mentioned in the text. SAGE can
infer attribute values even when such values are mentioned implicitly using
periphrastic language, or not-at-all-as is the case for common-sense defaults.
Additionally, SAGE is capable of predicting whether an attribute is
inapplicable for the product at hand, or non-obtainable from the available
information. SAGE is the first method able to tackle all aspects of the
attribute-value-prediction task as they arise in practical settings in
e-Commerce catalogs. A comprehensive set of experiments demonstrates the
effectiveness of the proposed approach, as well as, its superiority against
state-of-the-art competing alternatives. Moreover, our experiments highlight
SAGE's ability to tackle the task of predicting attribute values in zero-shot
setting; thereby, opening up opportunities for significantly reducing the
overall number of labeled examples required for training.