# TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation

arXiv: https://arxiv.org/abs/2601.04202 · HTML: https://mlsanity.com/paper/2601.04202/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [TableBench: A Comprehensive and Complex Benchmark for Table Question Answering](https://mlsanity.com/paper/2408.09174/) (2024)
- [Large Language Models for Telecom: Forthcoming Impact on the Industry](https://mlsanity.com/paper/2308.06013/) (2023)
- [TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications](https://mlsanity.com/paper/2406.01768/) (2024)
- [Table-R1: Inference-Time Scaling for Table Reasoning](https://mlsanity.com/paper/2505.23621/) (2025)
- [TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering](https://mlsanity.com/paper/2506.03949/) (2025)
- [Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents](https://mlsanity.com/paper/2501.13954/) (2025)
- [Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing](https://mlsanity.com/paper/2505.11856/) (2025)

## Built on by (most cited first, 6 of 6)

- [6G-Bench: An Open Benchmark for Semantic Communication and Network-Level Reasoning with Foundation Models in AI-Native 6G Networks](https://mlsanity.com/paper/2602.08675/) (2026)
- [TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications](https://mlsanity.com/paper/2605.09929/) (2026)
- [TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?](https://mlsanity.com/paper/2605.18025/) (2026)
- [TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks](https://mlsanity.com/paper/2609.25356/) (2026)
- [TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack](https://mlsanity.com/paper/2608.26126/) (2026)
- [OTel: Open Telco AI Datasets, Benchmarks, and Models](https://mlsanity.com/paper/2610.07766/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly applied to telecom engineering tasks, yet perform poorly on 3GPP specifications. These standards encode much of their technical information in complex tables, but LLM knowledge and interpretation of such tables remain largely unexplored. We introduce TeleTables, a benchmark comprising 2,220 tables from 13 3GPP specifications in four formats and 500 human-verified MCQs spanning direct retrieval to multi-step reasoning. Evaluating 20 open-weight LLMs across non reasoning, multimodal, reasoning, and table specialized architectures reveals two distinct performance bottlenecks. In the closed-book setting, domain knowledge is the primary constraint, with no general-purpose model exceeding 41% accuracy. When the table is provided as context, the best models exceed 90%, but performance degrades systematically with reasoning depth, evidence scope, and structural complexity, with a 32.2pp spread across reasoning skills. Table specialization on non-telecom data provides no consistent benefit, while strong reasoning capabilities remain essential for reliable interpretation of complex technical tables.