1 citations · 1 across the 14 of their papers we have counts for
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CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN
Pranshav Gajjar, Vijay K Shah
The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform reasoning over real-time teleme…
TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications
Pranshav Gajjar, Emmanuel Ojo, Vijay K Shah
Deploying large language models in telecommunications requires more than task accuracy. In realistic workflows, a model may inherit partially completed reasoning from a prior step,…
TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications
Pranshav Gajjar, Vijay K Shah
Large language models (LLMs) are increasingly deployed in the telecommunications domain for critical tasks, relying heavily on Retrieval-Augmented Generation (RAG) to adapt general…
LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models
Pranshav Gajjar, Manan Tiwari, Sayanta Seth +1
Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting labeled Radio Frequency (RF)…
Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling
Anton Saenko, Pranshav Gajjar, Abiodun Ganiyu +1
Large Language Models (LLMs) are increasingly applied to complex telecommunications tasks, including 3GPP specification analysis and O-RAN network troubleshooting. However, a criti…