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Extracting knowledge from unstructured or semi-structured textual information is essential for the machine learning applications that power DoorDash's search experience, and the development and maintenance of its product knowledge graph. Large language models (LLMs) have opened up new possibilities for utilizing their power in these areas, replacing or complementing traditional natural language processing methods. LLMs are also proving to be useful in the label and annotation generation process, which is critical for these use cases. In this talk, we will provide a high-level overview of how we incorporated LLMs for search relevance and product understanding use cases, as well as the key lessons learned and challenges faced during their practical implementation.
Das et al. (Mon,) studied this question.