Akshay Jagatap
2024
PEARL: Preference Extraction with Exemplar Augmentation and Retrieval with LLM Agents
Vijit Malik
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Akshay Jagatap
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Vinayak S Puranik
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Anirban Majumder
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
Identifying preferences of customers in their shopping journey is a pivotal aspect in providing product recommendations. The task becomes increasingly challenging when there is a multi-turn conversation between the user and a shopping assistant chatbot. In this paper, we tackle a novel and complex problem of identifying customer preferences in the form of key-value filters on an e-commerce website in a multi-turn conversational setting. Existing systems specialize in extracting customer preferences from standalone customer queries which makes them unsuitable to multi-turn setup. We propose PEARL (Preference Extraction with ICL Augmentation and Retrieval with LLM Agents) that leverages collaborative LLM agents, generates in-context learning exemplars and dynamically retrieves relevant exemplars during inference time to extract customer preferences as a combination of key-value filters. Our experiments on proprietary and public datasets show that PEARL not only improves performance on exact match by ~10% compared to competitive LLM-based baselines but additionally improves inference latency by ~110%.
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