Automatic extraction of attribute preferences from search queries is a critical problem in providing accurate product recommendations to customer. The task becomes even more challenging in cold-start settings where we do not have any supervised/labelled data available to train ML models. In this work, we implement a novel dataset generation pipeline (LLM-API) that leverages Large Language Models (LLMs), search logs and proprietary product information data from an e-commerce website to create a high quality dataset. Our proposed pipeline of LLM-API is robust as it can generalize to any product category with minimal changes in the LLM prompts. For the problem of converting product search queries to API calls we propose a multitask schema generator model which we train on our generated dataset. Experiments on an internal test set reveals that our proposed model achieves an improvement of ≈ 9.6% and ≈ 5% in Exact Match and Micro-F1 respectively, over competitive baselines. Benchmarking our approach on public test set of search queries further reveals a gain of ≈ 8.6% and ≈ 10.5% in Exact Match and Micro-F1. We further demonstrate that our approach outperforms a state-of-the-art LLM (Claude) applied on our task using few-shot prompting and CoT reasoning, while at the same time, achieves improvement in inference latency.
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