Large Language Models for Relevance Judgment in Product Search
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914874085343232 |
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| author | Mehrdad, Navid Mohapatra, Hrushikesh Bagdouri, Mossaab Chandran, Prijith Magnani, Alessandro Cai, Xunfan Puthenputhussery, Ajit Yadav, Sachin Lee, Tony Zhai, ChengXiang Liao, Ciya |
| author_facet | Mehrdad, Navid Mohapatra, Hrushikesh Bagdouri, Mossaab Chandran, Prijith Magnani, Alessandro Cai, Xunfan Puthenputhussery, Ajit Yadav, Sachin Lee, Tony Zhai, ChengXiang Liao, Ciya |
| contents | High relevance of retrieved and re-ranked items to the search query is the cornerstone of successful product search, yet measuring relevance of items to queries is one of the most challenging tasks in product information retrieval, and quality of product search is highly influenced by the precision and scale of available relevance-labelled data. In this paper, we present an array of techniques for leveraging Large Language Models (LLMs) for automating the relevance judgment of query-item pairs (QIPs) at scale. Using a unique dataset of multi-million QIPs, annotated by human evaluators, we test and optimize hyper parameters for finetuning billion-parameter LLMs with and without Low Rank Adaption (LoRA), as well as various modes of item attribute concatenation and prompting in LLM finetuning, and consider trade offs in item attribute inclusion for quality of relevance predictions. We demonstrate considerable improvement over baselines of prior generations of LLMs, as well as off-the-shelf models, towards relevance annotations on par with the human relevance evaluators. Our findings have immediate implications for the growing field of relevance judgment automation in product search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Relevance Judgment in Product Search Mehrdad, Navid Mohapatra, Hrushikesh Bagdouri, Mossaab Chandran, Prijith Magnani, Alessandro Cai, Xunfan Puthenputhussery, Ajit Yadav, Sachin Lee, Tony Zhai, ChengXiang Liao, Ciya Information Retrieval Artificial Intelligence H.3.3; I.2.7 High relevance of retrieved and re-ranked items to the search query is the cornerstone of successful product search, yet measuring relevance of items to queries is one of the most challenging tasks in product information retrieval, and quality of product search is highly influenced by the precision and scale of available relevance-labelled data. In this paper, we present an array of techniques for leveraging Large Language Models (LLMs) for automating the relevance judgment of query-item pairs (QIPs) at scale. Using a unique dataset of multi-million QIPs, annotated by human evaluators, we test and optimize hyper parameters for finetuning billion-parameter LLMs with and without Low Rank Adaption (LoRA), as well as various modes of item attribute concatenation and prompting in LLM finetuning, and consider trade offs in item attribute inclusion for quality of relevance predictions. We demonstrate considerable improvement over baselines of prior generations of LLMs, as well as off-the-shelf models, towards relevance annotations on par with the human relevance evaluators. Our findings have immediate implications for the growing field of relevance judgment automation in product search. |
| title | Large Language Models for Relevance Judgment in Product Search |
| topic | Information Retrieval Artificial Intelligence H.3.3; I.2.7 |
| url | https://arxiv.org/abs/2406.00247 |