Synthetic Test Collections for Retrieval Evaluation¶
Arxiv Link - 2024-05-13 14:11:09
Abstract¶
Test collections play a vital role in evaluation of information retrieval (IR) systems. Obtaining a diverse set of user queries for test collection construction can be challenging, and acquiring relevance judgments, which indicate the appropriateness of retrieved documents to a query, is often costly and resource-intensive. Generating synthetic datasets using Large Language Models (LLMs) has recently gained significant attention in various applications. In IR, while previous work exploited the capabilities of LLMs to generate synthetic queries or documents to augment training data and improve the performance of ranking models, using LLMs for constructing synthetic test collections is relatively unexplored. Previous studies demonstrate that LLMs have the potential to generate synthetic relevance judgments for use in the evaluation of IR systems. In this paper, we comprehensively investigate whether it is possible to use LLMs to construct fully synthetic test collections by generating not only synthetic judgments but also synthetic queries. In particular, we analyse whether it is possible to construct reliable synthetic test collections and the potential risks of bias such test collections may exhibit towards LLM-based models. Our experiments indicate that using LLMs it is possible to construct synthetic test collections that can reliably be used for retrieval evaluation.
Socials¶
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| 🚀 Exciting developments in the world of Information Retrieval (IR) systems! A recent study delves into the potential of using Large Language Models (LLMs) to construct synthetic test collections for evaluation purposes. 🔍 Generating diverse user queries and relevance judgments for test collections can be a challenge, but leveraging LLMs offers a promising solution. By creating synthetic queries and judgments, researchers are exploring the possibility of enhancing the evaluation process without the traditional resource-intensive methods. 📊 The study thoroughly investigates the feasibility of using LLMs to construct fully synthetic test collections and evaluates the reliability of such collections. The results suggest that synthetic test collections generated using LLMs can indeed be reliable for evaluating IR systems. 🔗 Dive deeper into the details of this innovative research at: Read more #ArtificialIntelligence #NLP #LLMs #InformationRetrieval #Research #TechInnovation |
🚀 Exciting research on using Large Language Models (LLMs) to construct synthetic test collections for Information Retrieval evaluation. Discover how LLMs can generate synthetic queries and relevance judgments efficiently! Check out the study here: http://arxiv.org/abs/2405.07767v1 #AI #NLP #LLMs #InformationRetrieval #TechResearch |