In today’s rapidly evolving large language model (LLM) landscape, technology companies such as Cisco face the difficult challengeof selecting the most suitable model for downstream tasks that demand deep, domain-specificproduct knowledge. Specialized benchmarks not only inform this decision making but alsocan be leveraged to rapidly create quizzes that can effectively train engineering and marketingpersonnel on novel product offerings in a continually growing Cisco product space.We present Pro-QuEST, our Prompt-chain based Quiz Engine using state-of-the-art LLMsfor generating multiple-choice questions on Specialized Technical products. In Pro-QuEST,we first identify key terms and topics from a given professional certification textbook orproduct guide, and generate a series of multiple-choice questions using domain-knowledgeguided prompts. We show LLM benchmarking results with the question benchmarks generated by Pro-QuEST using a range of latestopen-source, and proprietary LLMs and compare them with expert-created exams and review questions to derive insights on their composition and difficulty. Our experiments indicate that though there is room for improvementin Pro-QuEST to generate questions of the complexity levels seen in expert-designed certification exams, question-type based prompts provide a promising direction to address this limitation. In sample user studies with Cisco personnel, Pro-QuEST was received with high optimism for its practical usefulness in quicklycompiling quizzes for self-assessment on knowledge of novel products in the rapidly changing tech sector.

In this paper, we describe models developed by our team, NUS-IDS, for the Closed data track at the Arabic Modeling In Your Accent (AMIYA) shared task at VarDial 2026. The core idea behind our solution involves data augmentation enabled by a dialect classifier trained on AMIYA data. We effectively combine various translation, summarization, and question answering prompts with AMIYA training data to form dialectal prompts for use with state-of-the-art LLMs. Next, dialect predictions from our classifier on outputs from these LLMs are used to compile preference data for Reinforcement Learning (RL). We report model performance on dialectal Arabic from Egypt, Morocco, Palestine, Saudi Arabia and Syria using FLORES+, a multilingual machine translation dataset. Our experiments illustrate that though our RL models show significant performance gains on dialectness scores, they under perform on translation metrics such as chrF++ compared to base LLMs.

In a technology company, quality of customer service that involves providingtroubleshooting assistance and advice to customers is a crucial asset.Often, insights from historical customer service data are used to make decisions related to future product offerings. In this paper, we address the challenging problem of automatic assignment of product names and software version labels to customer Service Requests (SRs) related to BLIND, a company in the networking domain.We study the effectiveness of state-of-the-art Large Language Models (LLMs) in assigning the correct product name codes and software versions from several possible label options and their “non-canonical” mentions in the associated SR data. To this end, we frame the assignment as a multiple-choice question answering task instead of conventional prompts and devise, to our knowledge, a novel pipeline of employing a classifier for filtering inputs to the LLM for saving usage costs. On our experimental dataset based on real SRs, we are able to correctly identify product name and software version labels when they are mentioned with over 90% accuracy while cutting LLM costs by ~40-60% on average, thus providing a viable solution for practical deployment.