Feb. 28, 2024, 5:49 a.m. | Orion Weller, Kyle Lo, David Wadden, Dawn Lawrie, Benjamin Van Durme, Arman Cohan, Luca Soldaini

cs.CL updates on arXiv.org arxiv.org

arXiv:2309.08541v2 Announce Type: replace-cross
Abstract: Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for …

abstract arxiv cs.ai cs.cl cs.ir dataset datasets document expansion generative information language language models large language large language models lms query retrieval study type

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