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Observations on LLMs for Telecom Domain: Capabilities and Limitations
Sumit Soman, Ranjani H G
TL;DR
Enterprise wireless chat assistants must handle domain terminology and conversation context, but the suitability of LLMs for these tasks is uncertain. The paper compares four models using Cradlepoint information across domain and product questions, context continuity, and input perturbations. It finds varied strengths across tasks and concludes that publicly available models are not directly ready for enterprise use cases.
Problem
The paper examines whether LLM conversational assistants can handle enterprise wireless terminology and context, including whether generated recipes are reliable.
Source: §1 · pp. 1–2Method
The authors evaluate four LLMs using Cradlepoint information across domain and product questions, context continuity, and spelling or language perturbations.
Source: §2.1 · p. 2, §2.3 · p. 3, §4 · p. 9Results
Bard and GPT4 show promise for domain questions, GPT4 is assessed as most suitable for context retention, and Bard appears closer to the assessed summarization requirements.
Source: §4 · p. 9Takeaways & Limitations
The authors conclude that enterprise use requires domain-specific fine-tuning, and that character-aware features may help discern product names.
Source: §4 · pp. 9–10The conclusions are based on limited datasets, and enterprise deployment considerations such as licensing, data security, privacy, and costs remain to be assessed.
Source: §4 · pp. 9–10
Abstract
from arXiv · showhide
The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In this paper, we analyze capabilities and limitations of incorporating such models in conversational interfaces for the telecommunication domain, specifically for enterprise wireless products and services. Using Cradlepoint's publicly available data for our experiments, we present a comparative analysis of the responses from such models for multiple use-cases including domain adaptation for terminology and product taxonomy, context continuity, robustness to input perturbations and errors. We believe this evaluation would provide useful insights to data scientists engaged in building customized conversational interfaces for domain-specific requirements.
1 Introduction
This paper examines whether generative LLMs can serve as conversational assistants for enterprise wireless products and services, where users need help with product information and support. It evaluates domain terminology, context retention, recipe reliability, and robustness to language errors.
1 Introduction
- Enterprise wireless assistants need to understand domain terminology, concepts, and conversational context to support users seeking product information, configuration help, and troubleshooting.The paper frames these interfaces as a first line of interaction with end users.Source: §1 · p. 1
- The study focuses on enterprise wireless but notes that its approach and findings may extend to other domains interested in domain-specific conversational models.Source: §1 · p. 2
- The study asks whether LLM interfaces adapt to domain-specific terminology, retain context, produce reliable recipes, and withstand language or grammatical perturbations.The questions include telecom and product queries, co-reference and long-term context retention, hallucination, and domain adaptation.Source: §1 · p. 2
- The authors compare GPT4, GPT3.5, Bard, and LLaMA through experiments mapped to the research questions, using public Cradlepoint information and model chat interfaces.They describe the evaluation as initial observations before testing larger internal datasets.Source: §1 · p. 2
2 Experiments
The experiments assess four LLMs across telecom and product question answering, conversational context continuity, and robustness to language errors. They use Cradlepoint product information and shared prompts, with default model settings where available.
2 Experiments
- The evaluation groups question-answering tasks, product queries, two context-continuity scenarios, and spelling or language perturbations into experiments E1–E5.Source: §2 · p. 2
2.1 Domain Q&A
- Domain Q&A tests four 5G-related questions, including general telecom and modem-capability questions, with responses assessed for domain understanding and supplemented by opinion scores and inter-rater agreement.The authors note that evaluation may be subjective and that domain vocabulary may lack standardized taxonomies or SME support.Source: §2.1 · p. 2
2.2 Product Q&A
- Product Q&A uses seven questions about Cradlepoint products, including a nonexistent model placed near a valid one to examine factual discernment and response confidence.The questions also address product details, comparisons, and operational matters such as installation or troubleshooting.Source: §2.2 · p. 3
2.3 Context Continuity
- Context-continuity tests cover product-specification follow-ups and troubleshooting, requiring retention of earlier product information or router-problem context across queries.The product sequence also includes a typo in LTE bands and questions distinguishing product from model.Source: §2.3 · p. 3
2.4 Input Perturbations (Language Errors)
- Input-perturbation questions include general English errors and telecom terminology errors involving IKE and IPSec, with typos generated using keyboard distance.Source: §2.4 · p. 3
2.5 Parameters and Prompts Used
- The comparison uses common prompts and default settings for fairness, while the Bard trial interface and HuggingChat LLaMA interface lack configurable parameters.The E1 prompt directs models to answer telecom questions and avoid making up answers; E2–E5 restrict answers to Cradlepoint documentation.Source: §2.5 · p. 4
3 Results and Discussion
The results compare LLM responses across telecom-domain questions, product queries, context continuity, recipe generation, and input perturbations. GPT4 had the highest SME Mean Opinion Score across questions, but the findings also document factual errors, incomplete responses, and model-specific weaknesses.
3.1 RQ1 - E1 and E2
- Across domain questions, GPT4 gave detailed frequency-band and SA/NSA explanations, while Bard and GPT4 offered more elaborate mid-band use-case answers.For the LTE/5G and network-slicing question, GPT4 supplied the correct answer and justification; the other models associated steering with dual connectivity.Source: §3.1 · p. 5
3.1.1 MOS and Fleiss’ kappa
- GPT4 responses had the highest Mean Opinion Score across questions, while LLaMA had the lowest; Fleiss’ kappa was 0.316, indicating fair agreement across 16 categories.Five telecom SMEs independently rated responses for relevance, technical correctness, and completeness on a 1–5 scale.Source: §3.1.1 · pp. 5–6
- Product answers had factual and completeness gaps: all models gave incorrect IBR900 operating ranges, and none listed all eight ruggedized routers.For E300 expansion slots, GPT4 identified two correctly; in the E300/IBR900 comparison, GPT4 had four correct specification fields, versus two for GPT3.5 and none of Bard’s relevant fields.Source: §3.1.1 · p. 6
3.2 RQ2
- In context-retention tasks, GPT4 and Bard were more persistent and relevant overall, while ambiguous references could still yield ambiguous answers and required shared disambiguation.For the E4 troubleshooting scenario, 6 of 7 responses from GPT4 and Bard were SME-acceptable, compared with 3 of 7 for GPT3.5 and LLaMA.Source: §3.2 · p. 8
3.3 RQ3
- Recipe accuracy varied: Bard’s setup steps were 100% accurate, compared with GPT3.5 at 50%, GPT4 at 70%, and LLaMA at 0%.GPT4 provided more detailed steps than GPT3.5, but both included an incorrect access or activation step; the cited URLs were also incorrect or nonexistent.Source: §3.3 · pp. 8–9
- For fictitious product queries, GPT4 and LLaMA were comparatively less prone to hallucination, while GPT3.5 and Bard answered without flagging the product name as incorrect.The authors note that recipe steps may remain inaccurate even with prompt guardrails and suggest valid source URLs as a reliability measure.Source: §3.3 · pp. 8–9
3.4 RQ4
- Bard was more robust to input perturbations in the limited experiments; GPT4 also corrected typos and sometimes asked for clarification, while LLaMA was sensitive and its prompt guardrails did not hold.LLaMA resolved some errors but returned responses unrelated to Cradlepoint products; it also failed to provide configuration steps after resolving the IKE abbreviation.Source: §3.4 · p. 9
4 Conclusions and Future Work
The authors evaluate four popular LLMs on limited enterprise-wireless datasets and report promise in domain questions and context retention, alongside reliability risks. They conclude that publicly available models are not ready for direct enterprise use and require further domain-specific work.
Conclusions and Future Work
- The experiments use limited datasets, and the authors conclude that publicly available LLMs cannot be used directly for enterprise use cases.They call for fine-tuning and further experiments on hypothesized spurious correlations, as well as evaluation of licensing, data security and privacy, and deployment costs.Source: §4 · pp. 9–10
- Bard and GPT4 show promise for domain-question accuracy, but the authors say performance improvements require fine-tuning.Their conclusion also notes that access to recent data is needed for completeness in responses.Source: §4 · p. 9
- GPT4 was found most suitable for short- and long-term context retention, while the authors say a detailed study is desirable.They also recommend that LLMs ask for clarification on ambiguous queries, while noting that identifying ambiguity is itself a research problem.Source: §4 · p. 9
- The authors recommend that LLMs avoid providing URLs because their operational mode is restricted to training and inference; they hypothesize plugins or other interfaces could help.GPT3.5 and GPT4 may include the prompted domain, but the exact URL can still be incorrect.Source: §4 · p. 9
- Bard appears closer to the assessed requirements for reliable summarization, but the authors call for further study to confirm this initial observation.Source: §4 · p. 9
- The authors caution that product-name hallucinations remain a concern and suggest character-aware features may be needed to distinguish product names.Source: §4 · p. 9
- The authors report that LLMs appear robust to domain-related spelling perturbations, possibly because of context from the query or earlier conversation.Source: §4 · p. 9