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Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval paper by Independent / Community
⭐ 63.2
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Paper

Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval

by Independent / Community 02cc2eb4618442db82d38217a90b79a0bf99dba2
Free2AITools Nexus Index
63.2
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 70
P: Popularity 45
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

This study examines the use of Natural Language Processing (NLP) technology within the Islamic domain, focusing on developing an Islamic neural retrieval model. By leveraging the robust XLM-R model, the research employs a language reduction technique to create a lightweight bilingual large language model (LLM). Our approach for domain adaptation addresses the unique challenges faced in the Islamic domain, where substantial in-domain corpora exist only in Arabic while limited in other language...

Source β†’
Semantic Scholar 4 Citations
Paper Information Summary
Entity Passport
Registry ID 02cc2eb4618442db82d38217a90b79a0bf99dba2
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{02cc2eb4618442db82d38217a90b79a0bf99dba2,
  author = {Unknown},
  title = {Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/02cc2eb4618442db82d38217a90b79a0bf99dba2}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval [Paper]. Free2AITools. https://api.semanticscholar.org/02cc2eb4618442db82d38217a90b79a0bf99dba2

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 70
Popularity (P) 45
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval: Authority (A:70), Popularity (P:45), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
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πŸ“ Executive Summary

"This study examines the use of Natural Language Processing (NLP) technology within the Islamic domain, focusing on developing an Islamic neural retrieval model. By leveraging the robust XLM-R model, the research employs a language reduction technique to create a lightweight bilingual large language model (LLM). Our approach for domain adaptation addresses the unique challenges faced in the Islamic domain, where substantial in-domain corpora exist only in Arabic while limited in other language..."

❝ Cite Node 

@article{Unknown2026Multi-stage,
  title={Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ4CitationsSemantic Scholar
πŸ›οΈ70AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈknowledge retrievalField

🏷️ Research Topics

rag retrieval
πŸ“¦Data Source: semantic_scholar
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πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
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πŸ“Š Engagement & Metrics

downloads
0
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citations
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