ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering
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. To this end, we ask whether a fixed-generator, query-adapted RAG system can outperform generator-side adaptation, and which retriever objectives best support that setting. We motivate retrieval, rather than generator fine-tuning, as the adaptation...
| Authors | Heshan Fernando, Quan Xiao, Yan Xin et al. |
|---|---|
| Primary category | cs.IR |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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