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TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models

Hang Zou, Qiyang Zhao, Yu Tian, Lina Bariah, Faouzi Bader, Thierry Lestable, Merouane Debbah

arXiv:2407.09424v1eess.SPcs.AI

TL;DR

Mainstream LLMs generally lack specialized telecom knowledge, while telecom-domain training data and evaluation benchmarks are limited. The paper builds datasets, benchmarks, and a three-stage adaptation pipeline; its resulting models outperform corresponding base models and larger state-of-the-art models in the reported experiments.

  • Problem

    Mainstream LLMs lack telecom-specific knowledge, and the paper identifies a need for benchmarks to assess their capabilities in the telecom context.

    Source: §I · p. 1, B. Contributions · p. 2

  • Method

    The paper builds telecom pretraining, instruction, and preference datasets for continual pretraining, instruction tuning, and alignment tuning, and proposes telecom-specific evaluation benchmarks.

    Source: B. Contributions · p. 2, A. Pre-training Dataset - OpenTelecom · pp. 4–5

  • Results

    Models from the telecom-specific fine-tuning pipeline outperform corresponding base models and state-of-the-art models with significantly larger model sizes.

    Source: §VII · p. 12

  • Takeaways & Limitations

    The proposed benchmarks evaluate telecom LLM capabilities including knowledge queries, math modeling, technical-document classification, and code generation and analysis.

    Source: B. Contributions · p. 2

Abstract

from arXiv · show

Large Language Models (LLMs) have the potential to revolutionize the Sixth Generation (6G) communication networks. However, current mainstream LLMs generally lack the specialized knowledge in telecom domain. In this paper, for the first time, we propose a pipeline to adapt any general purpose LLMs to a telecom-specific LLMs. We collect and build telecom-specific pre-train dataset, instruction dataset, preference dataset to perform continual pre-training, instruct tuning and alignment tuning respectively. Besides, due to the lack of widely accepted evaluation benchmarks in telecom domain, we extend existing evaluation benchmarks and proposed three new benchmarks, namely, Telecom Math Modeling, Telecom Open QnA and Telecom Code Tasks. These new benchmarks provide a holistic evaluation of the capabilities of LLMs including math modeling, Open-Ended question answering, code generation, infilling, summarization and analysis in telecom domain. Our fine-tuned LLM TelecomGPT outperforms state of the art (SOTA) LLMs including GPT-4, Llama-3 and Mistral in Telecom Math Modeling benchmark significantly and achieve comparable performance in various evaluation benchmarks such as TeleQnA, 3GPP technical documents classification, telecom code summary and generation and infilling.

I. INTRODUCTION

Mainstream LLMs can support telecom applications, but their limited telecom knowledge remains a barrier even when model-size and latency concerns are mitigated. The paper situates this gap amid prior domain-specific models, telecom applications, and data and content challenges.

I. INTRODUCTION

  • LLMs are presented as potential tools for 6G applications such as radio resource management and adaptive communication protocols.The paper contrasts their generalization capabilities with conventional deep learning techniques already used in telecom.Source: §I · p. 1
  • Telecom-specific knowledge remains a fundamental challenge after model-size and inference-latency concerns are mitigated.The paper identifies this knowledge gap as the core problem it addresses.Source: §I · p. 1

A. Related Works

  • Prior domain-specific LLMs include financial, math, medical, legal, and code models, using pretraining, fine-tuning, or retrieval-augmented generation.Examples include BloombergGPT, FinGPT, WizardMath, MedPalM2, ChatLaw, SaulLM, and Code Llama.Source: A. Related Works · pp. 1–2
  • Telecom LLM work spans fault analysis, technical-document classification, network configuration, wireless hardware development, and envisioned network paradigms.The paper describes these early explorations as a shift from conventional deep neural network approaches.Source: A. Related Works · p. 2
  • GPT-4 fails almost half of specification-related TeleQnA questions, highlighting the gap between mainstream models and telecom standards.Telecom networks rely on protocols and standards from bodies including 3GPP, IEEE, and ITU.Source: A. Related Works · p. 2
  • Building telecom-specific LLMs is hindered by scarce open training datasets, technical-document-heavy text, multimodal content, and frequent knowledge updates.The paper favors adapting general-purpose models over pretraining from scratch, given the latter's cost and update constraints.Source: A. Related Works · p. 2

B. Contributions

The paper contributes telecom-specific datasets, benchmarks, and a pipeline for adapting general-purpose LLMs. Its continual pretraining stage uses causal language modeling to specialize a base model at lower cost than pretraining from scratch.

B. Contributions

  • The paper builds telecom-specific pretraining and instruction datasets and proposes benchmarks for knowledge queries, math modeling, technical-document classification, and code tasks.The benchmarks cover code generation and analysis alongside other telecom capabilities.Source: B. Contributions · p. 2
  • The contributions include a pipeline for adapting general-purpose LLMs and a telecom-specific model intended for multiple downstream tasks.The pipeline comprises continual pretraining, instruction tuning, and alignment tuning.Source: B. Contributions · p. 2

A. Telecom-Specific Continual Pre-training

  • Continual pretraining offers a lower-cost alternative to training a domain-specific model from scratch.The method continues training a general-purpose model on telecom-domain text.Source: A. Telecom-Specific Continual… · pp. 2–3
  • The continual-pretraining objective is causal language modeling: predicting each next token conditioned on preceding tokens.The passage defines x_<t as the token sequence before token x_t.Source: A. Telecom-Specific Continual… · p. 3

B. Telecom-Specific Instruct Tuning

The paper uses supervised fine-tuning to teach telecom instruction following, then applies direct preference optimization to align responses. The latter uses chosen and rejected answers rather than an explicit reward model.

B. Telecom-Specific Instruct Tuning

  • Supervised fine-tuning trains the model on instruction-response pairs to minimize response negative log-likelihood conditioned on the instruction.The paper says instruction tuning supports following instructions even on unseen tasks without explicit examples.Source: B. Telecom-Specific Instruct Tuning · p. 3

C. Telecom-Specific Alignment Tuning

  • Direct preference optimization aligns the supervised fine-tuned model using chosen and rejected response pairs, avoiding an explicit reward function.The reference policy is the supervised fine-tuned model from the previous stage.Source: C. Telecom-Specific Alignment Tuning · pp. 3–4

III. DATASET

The section defines three datasets for continual pretraining, instruction tuning, and alignment tuning, then details OpenTelecom’s sources and telecom-content filtering.

III. DATASET

  • The training pipeline uses pretraining, instruction, and preference datasets for continual pretraining, instruction tuning, and alignment tuning, respectively.The pipeline figure names continual pretraining on telecom data, SFT, and alignment tuning as its three stages.Source: §III · p. 4, Figure 1 · p. 5

A. Pre-training Dataset - OpenTelecom

  • OpenTelecom draws on telecom standards, research papers, books, patents, QAs, wiki, and code, with separate preprocessing pipelines by source.The standards collection includes 3GPP releases 8–19 and selected IEEE standards; preprocessing removes HTML, URLs, tables, references, and limited-information drafts and change requests.Source: A. Pre-training Dataset - OpenTelecom · p. 4
  • For general-corpus filtering, the framework applies keyword filtering followed by deduplication, using a non-exhaustive list of 700 telecom keywords.The keywords are selected against six criteria, and the framework also uses keyword count and density to assess a sample’s telecom relevance.Source: A. Pre-training Dataset - OpenTelecom · p. 4
  • Keyword selection favors timely, telecom-specific terms and avoids ambiguous terms and noisy abbreviations such as DL and SAP.The keyword list includes abbreviations, but abbreviations that frequently introduce noise are removed after testing.Source: A. Pre-training Dataset - OpenTelecom · pp. 4–5
  • Telecom keyword density is M/log(N + 1), with M matches across N words, to compensate for document length when estimating relevance.The authors also use total keyword count to reduce false positives.Source: A. Pre-training Dataset - OpenTelecom · p. 5
  • Deduplication removes redundant content, which the authors consider necessary because standards overlap across organizations and releases.They apply the existing xorbits deduplication tool.Source: A. Pre-training Dataset - OpenTelecom · p. 5

B. Instruct Tuning Dataset - TelecomInstruct

TelecomInstruct is designed around diverse telecom tasks, ranging from explaining concepts and standards to technical question answering, modeling, and code work.

B. Instruct Tuning Dataset - TelecomInstruct

  • Telecom Instruct is designed around diverse critical telecom-domain tasks, reflecting the stated view that task diversity matters more than simply increasing the number of instructions of the same type.Protocol workflows follow human prompts; Tdoc classification, math modeling, and code infilling can be built from raw text, papers, and code, while remaining tasks use GPT3.5 or GPT4 generation with expert filtering and validation.Source: B. Instruct Tuning Dataset -… · pp. 5–6
  • Question-answering tasks include selecting all correct MCQ options and answering telecom questions openly from standards, papers, or patents.The dataset also includes classification of technical documents into their related standards-development working groups.Source: B. Instruct Tuning Dataset -… · p. 5
  • Math-modeling instructions ask for equations such as channel models, while code generation and infilling target telecom functions and incomplete scripts.Code infilling supplies context and a target functionality for completing a script.Source: B. Instruct Tuning Dataset -… · p. 6
  • Code-summary instructions capture a script’s core functionality and telecom relevance, while code-analysis instructions explain its operational logic and telecom principles.Source: B. Instruct Tuning Dataset -… · p. 6
  • General instructions cover explaining concepts, describing specifications, identifying problems, proposing solutions, and summarizing telecom standards, patents, and papers.Protocol instructions generate telecom-standard workflows from human prompts.Source: B. Instruct Tuning Dataset -… · p. 6

C. Alignment Tuning Dataset - TelecomAlign

TelecomAlign targets undesired SFT responses with a stated preference for concise, accurate answers, and the evaluation section argues for assessing capabilities beyond MCQ accuracy.

C. Alignment Tuning Dataset - TelecomAlign

  • After SFT, the authors observed repeated, overly short, and telecom-irrelevant responses, motivating alignment toward concise and accurate answers.The stated preference is to use the minimum information unless more is requested.Source: C. Alignment Tuning Dataset -… · p. 6
  • TelecomAlign selects instructions with low performance metrics, then uses pretrained LLMs to select preferred responses under a clear preference description.The authors describe Rouge scores and response length relative to the ground truth as possible measures.Source: C. Alignment Tuning Dataset -… · p. 6

IV. EVALUATION BENCHMARKS

  • The evaluation section identifies few telecom-specific benchmarks and argues that high MCQ accuracy alone may not capture practical downstream capabilities.It calls for benchmarks covering varied telecom tasks and applications.Source: §IV · p. 6

A. Telecom Question Answering

The evaluation methods span telecom question answering, technical-document classification, and mathematical modeling, with the latter assessed through equation infilling and similarity scoring.

A. Telecom Question Answering

  • The MCQ benchmark uses GPT-4 to draft questions from cleaned telecom texts, GPT-3.5 to validate them against the source, and human validation for retained pairs.References are removed from source texts to avoid questions tied to specific literature.Source: A. Telecom Question Answering · pp. 6–7
  • The open-ended QA benchmark removes answer options and scores generated answers against ground truth with Rouge, making evaluation closer to real QA use.The dataset is also used to align models with preferred telecom responses.Source: A. Telecom Question Answering · p. 7

B. Telecom Standard Documents Classification

  • Technical-document understanding matters for network decisions involving scenarios, valid action ranges, and the effects of actions on other entities.The benchmark asks models to classify text into one of 15 3GPP working groups without context; the authors report that this is difficult for mainstream models in zero-shot settings.Source: B. Telecom Standard Documents… · p. 7

C. Telecom Math Modeling

  • Telecom math modeling requires domain knowledge and factual reasoning, while flexible assumptions and varied models make direct evaluation of generated formulations challenging.Source: C. Telecom Math Modeling · p. 7
  • The proposed math-modeling task masks crucial system-modeling or problem-formulation equations in relevant document paragraphs and asks the model to recover them.The model predicts one equation at a time, with context and ground-truth equations rather than earlier predictions supplied before the target.Source: C. Telecom Math Modeling · pp. 7–8
  • MathBERT evaluates predicted equations by cosine similarity of embeddings, normalized against the similarity to an empty answer.The authors motivate the normalization by noting that empty or random equations can otherwise receive high raw cosine similarity.Source: C. Telecom Math Modeling · p. 8

D. Telecom Code Understanding and Generation

The paper describes telecom code benchmarks and instruction-following datasets for applying LLMs to telecom tasks. These cover code understanding and generation, as well as general and protocol-based instructions.

D. Telecom Code Understanding and Generation

  • Telecom code benchmarks cover four tasks across C, C++, Python, and Matlab, reflecting the importance of code analysis and generation for telecom systems and simulators.Code-fixing and editing tasks are omitted for lack of relevant open-source datasets.Source: D. Telecom Code Understanding and… · p. 8
  • Code samples are selected for telecom relevance, and Rouge similarity to ground truth is used because script effectiveness is difficult to evaluate directly.For infilling, successive lines are replaced by a < FILL > placeholder; Rouge measures relevance, not true effectiveness.Source: D. Telecom Code Understanding and… · p. 8

E. Telecom Instruct Following

  • Telecom instruct-following tasks target productivity in telecom research, design, and development, including finding literature solutions and identifying implementation steps from standards.The dataset includes general instructions generated from standards, specifications, papers, and patents, plus protocol workflows based on human instructions; both receive model and human validation.Source: E. Telecom Instruct Following · p. 9

V. TRAINING DETAIL

Training uses base models of at most 8B parameters and combines continued telecom pretraining, instruction tuning, and preference alignment. The best reported TelecomGPT is an instruct-tuned Llama3-8B followed by alignment tuning.

V. TRAINING DETAIL

  • Base models are limited to 8B parameters by GPU resources; the selected families include Llama2-7B, Mistral-7B, and Llama3-8B.Continued pretraining uses OpenTelecom data on eight AWS ml.p4d.24xlarge instances for approximately six hours; Llama3-8B is not further pretrained because its training-data overlap is uncertain and hardware limits batch size.Source: §V · p. 9
  • Instruction tuning uses QLoRA, with a rank-512 adapter, α = 256, 0.05 dropout, and three training epochs.Training uses a constant 2.0 × 10^-4 learning rate and AdamW without weight decay; the prompt template guides clear sequential task-completion or planning steps without implicit references.Source: §V · p. 9, Figure 4 · p. 9
  • Alignment tuning uses QLoRA and DPO, with β = 0.1, a rank-256 adapter, α = 128, and one epoch of training.The stated aim of QLoRA is to keep the aligned model from deviating too far from the supervised fine-tuned model.Source: §V · p. 9
  • TelecomGPT is the best model from the pipeline’s stages, specifically an instruct-tuned Llama3-8B followed by alignment tuning.The paper names continual pretraining, instruction tuning, and DPO alignment as the pipeline stages.Source: §V · pp. 9–10

VI. RESULTS

Across telecom benchmarks, the tuned models show gains in math modeling, code tasks, and open-ended and instruction-following tasks, while results also expose limits in standard-specification questions and model scale. Training curves and reward margins show changes across the pipeline’s stages.

A. Traning Performance

  • Continual-pretraining evaluation loss drops substantially before gradually saturating, while instruction-tuning evaluation loss indicates learning across instruction types.The alignment-tuning reward margin shows chosen-like responses becoming preferred over rejected-like responses.Source: A. Traning Performance · p. 10, Figure 6 · p. 10, Figure 7 · p. 10

B. Evaluation Benchmark Performance

  • After instruction tuning, TeleQnA accuracy increases across base models; telecom-specific models outperform their instruct-tuned bases and are comparable to GPT-4, with a slight drop after alignment.For Llama2-7B, the authors report an approximately 4% accuracy increase after continual pretraining; standard-specification question accuracy remains limited across tested models.Source: B. Evaluation Benchmark Performance · p. 10, Table III · p. 11
  • Most telecom-specific LLMs reach near 70% average 3GPP document-classification accuracy, versus under 40% for GPT-4o and GPT-4.The test set contains 2,000 texts from all 16 working groups; RAN accuracy is significantly higher than SA, indicating imbalanced understanding across groups.Source: B. Evaluation Benchmark Performance · pp. 10–11, Table V · p. 11
  • TelecomGPT’s MathBERT-score CDF dominates GPT-4 above 60%, and alignment tuning slightly increases scores while outperforming GPT-4 and GPT-4o.The reported average MathBERT scores across LLMs are close to 50%, which the authors say demonstrates the benchmark’s difficulty.Source: B. Evaluation Benchmark Performance · pp. 11–12, Figure 8 · p. 12, Table VI · p. 12
  • Fine-tuned and aligned models have significantly higher Rouge scores than general-purpose LLMs across four telecom code tasks, while aligned code summaries follow the concise style of chosen answers.The authors note that telecom-specific models still hallucinate for some requests.Source: B. Evaluation Benchmark Performance · p. 12
  • Instruction tuning improves Rouge scores for Llama-3 and Mistral across open-ended QA and general and protocol instruction tasks; alignment further improves open-ended QA and general instructions.The reported examples show more precise and concise answers after instruction tuning and less confusion on general and protocol instructions after alignment.Source: B. Evaluation Benchmark Performance · p. 12

VII. CONCLUSION

The paper presents a pipeline for adapting general-purpose LLMs to telecom and reports gains over corresponding base models and larger state-of-the-art models. Its experiments are small-scale and text-only.

VII. CONCLUSION

  • The paper’s pipeline builds telecom-specific LLMs by adapting general-purpose models and reports performance above corresponding base models and larger state-of-the-art models.The pipeline combines telecom-relevant data selection, instruction and preference datasets, and benchmarks for telecom capabilities.Source: §VII · p. 12
  • Experiments remain small-scale because of resource limits; pretraining from scratch on a larger dataset is identified as a promising direction.Source: §VII · p. 12
  • The framework handles text only, with integration of other modalities—especially radio signals—proposed as future work.Source: §VII · p. 12

Llama3-8B-TI-TA

In the Llama3-8B-TI-TA evaluation, telecom-specific models are described as producing more accurate telecom math models than general-purpose models. Their instruct-tuned and aligned outputs also show telecom-focused behavior in code infilling and summaries.

Llama3-8B-TI-TA

  • Telecom-specific models provide more accurate math modeling of the received signal and channel than general-purpose models, which fail to follow notation or omit channel noise and add unsupported time variation.The received-signal comparison notes that no notation was predefined; the channel-model comparison presents three equations that were generated sequentially.Source: Table XVIII · p. 23, Table XIX · p. 24
  • In code infilling, the instruct-tuned model's missing lines are very close to ground truth, while the aligned model's response differs slightly.Source: Table XX · p. 25
  • In code summaries, instruct-tuned and aligned models are concise and consistently focus on telecom aspects, unlike the detailed but inconsistently focused base-model summary.Source: Table XXI · p. 26
  • Telecom-specific generated code captures most intended functionality, though it is less commented and differs from ground truth drawn from a complete, context-rich file.Source: Table XXII · p. 27
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