mlsanityData to 7 Oct 2026

Improving Domain-Specific Retrieval by NLI Fine-Tuning

arXiv 2308.03103 ↗First posted 6 Aug 2023cs.CL cs.IRcited by 1

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Abstract (arXiv)

The aim of this article is to investigate the fine-tuning potential of natural language inference (NLI) data to improve information retrieval and ranking. We demonstrate this for both English and Polish languages, using data from one of the largest Polish e-commerce sites and selected open-domain datasets. We employ both monolingual and multilingual sentence encoders fine-tuned by a supervised method utilizing contrastive loss and NLI data. Our results point to the fact that NLI fine-tuning increases the performance of the models in both tasks and both languages, with the potential to improve mono- and multilingual models. Finally, we investigate uniformity and alignment of the embeddings to explain the effect of NLI-based fine-tuning for an out-of-domain use-case.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 6 Aug 2023.

Builds on

16 of 20 references.

References · 16

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4 more
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Citing papers per year: 2023: 1.

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  1. 2023Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challengescited by 6