Hugging Face Trending Papers June 29, 2026

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

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Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language. In low-resource subdomains, generator fine-tuning can over-specialize and degrade general capability, making query-side retriever adaptation an attractive alternative.

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arXiv AI

Jun 30

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

arXiv:2606. 29706v1 Announce Type: cross Abstract: Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language.

By Heshan Fernando, Quan Xiao, Yan Xin, Tianyi Chen

rag nlp fine-tuning benchmarks

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llms rag nlp

Hugging Face Trending Papers

Jun 27

AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering

Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard ones, and gives no signal for when a generated answer can be trusted.

llms rag nlp

arXiv AI

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By Karan Singh, Michael Yu, Varun Gangal, Zhuofu Tao, Sachin Kumar, Emmy Liu, Steven Y. Feng

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ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

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