Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks in recent years. This has inspired researchers and practitioners in the real-world industrial domain to build useful products via leveraging LLMs. However, extensive evaluations of LLMs, in terms of accuracy, memory management, and inference latency, while ensuring the reproducibility of the results are crucial before deploying LLM-based solutions for real-world usage. In addition, when evaluating LLMs on internal customer data, an on-premise evaluation system is necessary to protect customer privacy rather than sending customer data to third-party APIs for evaluation. In this paper, we demonstrate how we build an on-premise system for LLM evaluation to address the challenges in the evaluation of LLMs in real-world industrial settings. We demonstrate the complexities of consolidating various datasets, models, and inference-related artifacts in complex LLM inference pipelines. For this purpose, we also present a case study in a real-world industrial setting. The demonstration of the LLM evaluation tool development would help researchers and practitioners in building on-premise systems for LLM evaluation ensuring privacy, reliability, robustness, and reproducibility.

Deploying Large Language Models (LLMs) for question answering (QA) over lengthy contexts is a significant challenge. In industrial settings, this process is often hindered by high computational costs and latency, especially when multiple questions must be answered based on the same context. In this work, we explore the capabilities of LLMs to answer multiple questions based on the same conversational context. We conduct extensive experiments and benchmark a range of both proprietary and public models on this challenging task. Our findings highlight that while strong proprietary LLMs like GPT-4o achieve the best overall performance, fine-tuned public LLMs with up to 8 billion parameters can surpass GPT-4o in accuracy, which demonstrates their potential for transparent and cost-effective deployment in real-world applications.

Punctuation restoration is a crucial step after Automatic Speech Recognition (ASR) systems to enhance transcript readability and facilitate subsequent NLP tasks. Nevertheless, conventional lexical-based approaches are inadequate for solving the punctuation restoration task in Spanish, where ambiguity can be often found between unpunctuated declaratives and questions. In this study, we propose a novel hybrid acoustic-lexical punctuation restoration system for Spanish transcription, which consolidates acoustic and lexical signals through a modular process. Our experiment results show that the proposed system can effectively improve F1 score of question marks and overall punctuation restoration on both public and internal Spanish conversational datasets. Additionally, benchmark comparison against LLMs (Large Language Model) indicates the superiority of our approach in accuracy, reliability and latency. Furthermore, we demonstrate that the Word Error Rate (WER) of the ASR module also benefits from our proposed system.

Sentiment analysis serves as a pivotal component in Natural Language Processing (NLP). Advancements in multilingual pre-trained models such as XLM-R and mT5 have contributed to the increasing interest in cross-lingual sentiment analysis. The recent emergence in Large Language Models (LLM) has significantly advanced general NLP tasks, however, the capability of such LLMs in cross-lingual sentiment analysis has not been fully studied. This work undertakes an empirical analysis to compare the cross-lingual transfer capability of public Small Multilingual Language Models (SMLM) like XLM-R, against English-centric LLMs such as Llama-3, in the context of sentiment analysis across English, Spanish, French and Chinese. Our findings reveal that among public models, SMLMs exhibit superior zero-shot cross-lingual performance relative to LLMs. However, in few-shot cross-lingual settings, public LLMs demonstrate an enhanced adaptive potential. In addition, we observe that proprietary GPT-3.5 and GPT-4 lead in zero-shot cross-lingual capability, but are outpaced by public models in few-shot scenarios.

Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications. In this paper, we propose a Spanish punctuation restoration system designed for a real-time customer support transcription service. To address the data sparsity of Spanish transcripts in the customer support domain, we introduce two transferlearning-based strategies: 1) domain adaptation using out-of-domain Spanish text data; 2) crosslingual transfer learning leveraging in-domain English transcript data. Our experiment results show that these strategies improve the accuracy of the Spanish punctuation restoration system.