TeleTables is a benchmark that evaluates large language models on interpreting telecom tables from 3GPP specifications. It contains 2,220 tables in four formats and 500 human‑verified multiple‑choice questions that range from simple retrieval to multi‑step reasoning. Tests on 20 open‑weight LLMs show that closed‑book performance is limited by domain knowledge, while providing the table as context yields high accuracy that still drops with deeper reasoning, evidence scope, and structural complexity.
arXiv:2506. 18421v3 Announce Type: replace-cross Abstract: The majority of data in businesses and industries is stored in tables, databases, and data warehouses.
TelecomGPT‑R1‑9B is an open‑source large language model designed specifically for telecom reasoning tasks. It was trained on a 67,427‑example supervised fine‑tuning corpus that covers protocol, knowledge, modeling, and fault reasoning, and further refined with a two‑stage post‑training process involving low‑rank adaptation and policy optimization. The model tops the GSMA open telco leaderboard and matches state‑of‑the‑art closed‑source reasoners across seven public telecom benchmarks.
arXiv:2411. 19504v2 Announce Type: replace Abstract: The advance of large language models (LLMs) has unlocked great opportunities in complex multi-modal data management tasks, particularly in question answering (QA) over complicated multi-table relational data.
PARTAB is a framework that improves large language model reasoning on tables by constructing a structured evidence interface. It represents query‑relevant evidence as semantically coherent, row‑linked table regions and performs hierarchical selection over column groups and row‑level partitions before composing the evidence for answer generation. Evaluations on multiple table reasoning benchmarks show that PARTAB consistently outperforms full‑table prompting and recent methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical reasoning tasks.
arXiv:2607. 20510v1 Announce Type: new Abstract: We introduce Telco-GAIA, a bilingual, multi-modal benchmark for evaluating tool-using agents on the data of a real-world telecommunications operator.
arXiv:2607. 11207v1 Announce Type: cross Abstract: Table-based reasoning with large language models (LLMs), which requires reasoning based on natural language questions and structured tabular data, has gained widespread attention.
arXiv:2605. 20254v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, because Table Question-Answering (TQA) requires precise cell retrieval and multi-step structured reasoning.
Large Language Models (LLMs) have shown strong capabilities in table reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and difficulty localizi...
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 telemetry. The state-of-th...
TabScope introduces a question‑adaptive framework for table question answering that dynamically chooses between localized and full‑table reasoning. It constructs question‑specific sub‑tables via operation‑aware decomposition and predicts the question type to select the appropriate reasoning mode. Experiments on WikiTQ and the new SLQA benchmark show that localization improves lookup and local reasoning questions, while adaptive selection yields the best overall performance on long tables.
arXiv:2607. 28680v1 Announce Type: cross Abstract: Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities.
The paper introduces CRAFT, a data‑centric fine‑tuning approach that aligns small language models (SLMs) for pre‑hoc reasoning in AI‑native 6G radio access networks (RAN). By automatically generating verified (input, trace, label) triplets and fine‑tuning with low‑rank adaptation, CRAFT achieves high accuracy and F1 scores on TRACTOR and IC xApp datasets while avoiding parse failures that plague RL methods like GRPO. It also reduces energy consumption by 59% compared to GRPO baselines, offering a more sustainable path to auditable AI in 6G RAN.