Value of Open Data Models in Telecom Industry

A Telco LLM? A Good Start, But We’re Thinking Too Small. Three years ago, I proposed the idea of a Telco-specific Large Language Model, an AI designed to leverage the ~400 terabytes of multimodal telecom data spanning network operations, customer insights, marketing intelligence, and regulatory knowledge. Back then, it was already clear: we had the data, the use cases, and the urgency. Now, GSMA has launched Open-Telco LLM Benchmarks, an open-source initiative aimed at improving LLM performance in telecom applications. Their results validate what many suspected: 📉 GPT-4 scores below 75% on TeleQnA, a benchmark for telecom knowledge. 📉 Even worse, it scores under 40% on 3GPP technical documentation classification. 📉 Smaller models, like Microsoft’s Phi-2, struggle even more—scoring just 10% on general math reasoning (MATH500). Clearly, off-the-shelf AI models fail in Telco-specific tasks. This initiative is a step forward, providing standardized benchmarks for capabilities, energy efficiency, and accuracy. But here’s the real question: Are We 10 Steps Behind? The AI landscape has already evolved beyond static LLMs. Leading AI research—including OpenAI (GPT-4o), Google DeepMind (Gemini), and Meta (Llama-3) is shifting towards multimodal AI stacks that integrate text, voice, video, IoT telemetry, real-time network traffic, and structured databases. A Telco LLM, in isolation, is not enough. 🔹 LLMs are just the text interface. Telco needs AI systems that can "see" network logs, "hear" operational alarms, and "act" on real-time anomalies. 🔹 AI must operate at the edge. Telcos can’t afford high-latency, cloud-dependent inference. AI models need to run on local network nodes, base stations, and edge data centers. 🔹 Open-source isn’t just about benchmarking models—it’s about democratizing AI. Why stop at evaluating AI models when we could be building Telco AI infrastructure, fine-tuned multimodal models, and AI-native network architectures? We Need a Different AI Vision for Telecom. ✅ Multimodal AI Architectures → AI that understands network logs, video feeds from cell sites, customer interactions, and operational workflows. ✅ AI Agents for Automated Network Operations → AI that detects anomalies, optimizes load balancing, and predicts failures before they happen. ✅ Open Knowledge Repositories → A Hugging Face-like initiative for Telco, providing curated datasets, domain-specific embeddings, and foundational models. ✅ Real-Time, On-Device Inference → AI models that process network data instantly—without relying on cloud APIs. The 8 million professionals working in this industry need more than just a text-based LLM. They need an open, multimodal AI ecosystem that drives real automation, real optimization, and real intelligence at every layer of the network. GSMA’s initiative is a solid start, but let’s not settle for the past. The future of AI in telecom is multimodal, edge-native, and deeply integrated into networks.

Unlocking the Power of Telco-Specific LLMs for Intent-Based Operations Artificial Intelligence is revolutionizing telecom operations, but existing AI models struggle with domain-specific queries. General-purpose LLMs often misinterpret telecom standards, provide inaccurate recommendations, and fail to support real-world network automation needs. This is where Telco-Specific LLMs step in—bridging the gap between AI potential and telecom’s complex operational landscape. Why Do We Need Telco-Specific LLMs? 1️⃣ Domain Expertise: Unlike generic AI models, telco-specific LLMs are fine-tuned on telecom datasets, enabling accurate responses for network operations, customer service, and policy enforcement. 2️⃣ Standards Alignment: These models are trained on industry frameworks like 3GPP and TM Forum, ensuring compliance with global telecom standards. 3️⃣ Intent-Based Operations: AI-driven automation in telecom requires LLMs that understand operator intent, translating high-level objectives into precise network actions—enhancing closed-loop automation, self-healing networks, and predictive assurance. GSMA’s Initiative: Open-Telco LLM Benchmarks Recognizing the limitations of existing AI models, GSMA has launched the Open-Telco LLM Benchmarks to improve AI model performance for telecom use cases. Key highlights: ✅ Industry-First Benchmarking Framework – Establishing standardized evaluation metrics for telecom AI performance. ✅ Transparent & Open-Source – Hosted on Hugging Face, promoting collaboration and innovation. ✅ Backed by Leading Telcos – Supported by Deutsche Telekom, SK Telecom, Turkcell, LG Uplus, and Huawei. What This Means for the Future of AI in Telecom With initiatives like GSMA’s Open-Telco LLM Benchmarks, the telecom industry is on the path to AI-driven, intent-based operations—where networks self-optimize, customer interactions become hyper-personalized, and automation becomes truly predictive. The future of telecom AI isn’t just about using LLMs—it’s about building the right LLMs for telecom. What’s your take on Telco-Specific LLMs? Let’s discuss! #AI #Telecom #LLM #GSMA #IntentBasedOperations #Automation

"Which AI model is best for telecom?" Finally, there's a real answer. GSMA just launched the Open-Telco LLM Benchmark. And it changes everything. WHAT IS IT? First AI evaluation framework built FOR telecom Not general trivia tests. Not consumer chatbot benchmarks. Real telecom tasks: → Network configuration → Log troubleshooting → 3GPP standards interpretation → Policy logic → Schema-driven operations THE 5 TEST DATASETS: 1. TeleYAML - Configuration file generation 2. TeleLogs - Network log analysis 3. TeleMath - RF calculations, capacity planning 4. TeleQnA - Telecom domain Q&A 5. 3GPP-TSG - Standards compliance & interpretation Tests what matters: Precision, reasoning, telecom context WHY THIS MATTERS: Before: → Operators testing ChatGPT, Claude, Gemini → No standardized way to compare → "Seems good" = evaluation methodology → Wasting millions on wrong models Now: → Objective scores on telecom tasks → Know which model handles YANG configs → Which understands 3GPP specs → Which can debug network logs AI hype → Engineering data THE PROBLEM IT SOLVES: General benchmarks (MMLU, ARC): → Test trivia, common knowledge → "What's the capital of France?" → Useless for telecom Can't evaluate: → Schema-driven syntax → Standards compliance → RF engineering math → Network troubleshooting logic Open-Telco Benchmark: → Tests telecom-specific intelligence → Real operational scenarios → Pass = Actually useful for networks WHAT'S NEXT: Operators can now: → Test models against same benchmark → Compare vendors objectively → Make data-driven AI decisions → Stop guessing Vendors must: → Optimize for telecom tasks → Publish benchmark scores → Compete on measurable performance THE BOTTOM LINE: GSMA just gave telecom an AI yardstick. No more: → "ChatGPT seems smart" → "Claude sounds good" → "Gemini might work" Now: → Model X scores 87% on TeleYAML → Model Y scores 92% on 3GPP-TSG → Make informed decisions AI for telecom just became measurable. Finally. Using AI in your network? → 🧪 Tested models? → 📊 Need benchmark? → 🤔 Which model winning? Share below 👇 Join my Free 5G/6G Learning Free whatsapp Channel : https://lnkd.in/gerTY-kr ♻️ Repost this to help your network get started ➕ Follow Nitin Gupta for more