PEARL: Preference Extraction with Exemplar Augmentation and Retrieval with LLM Agents (2024.emnlp-industry)
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| Challenge: | Existing systems specialize in extracting customer preferences from standalone queries . absence of a conversational interface often leaves customers feeling the need for humanlike assistance . |
| Approach: | They propose a shopping assistant chatbot that extracts customer preferences as key-value filters from a multi-turn conversation on an e-commerce website. |
| Outcome: | The proposed solution improves performance on exact match by 10% compared to baselines and improves inference latency by 1%. |
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