Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry

• URL: http://arxiv.org/abs/2404.17975v1
• Date: Sat, 27 Apr 2024 18:28:10 GMT
• Title: Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry
• Authors: Simone Barandoni, Filippo Chiarello, Lorenzo Cascone, Emiliano Marrale, Salvatore Puccio,
• Abstract summary: Large Language Models (LLMs) have emerged as powerful tools for extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor posts. Our findings highlight the efficacy of opensource LLMs, particularly Mistral 7B, in achieving comparable performance to larger closed models.
• Score: 2.4244694855867275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor posts. Leveraging a diverse range of models, including both open-source and proprietary ones such as GPT-4 and Gemini, we aim to elucidate their strengths and weaknesses in this specialized domain. Through an evaluation process involving metrics such as BERTScore, ROUGE, and BLEU, we assess the performance of each model in accurately identifying and summarizing customer needs. Our findings highlight the efficacy of opensource LLMs, particularly Mistral 7B, in achieving comparable performance to larger closed models while offering affordability and customization benefits. Additionally, we underscore the importance of considering factors such as model size, resource requirements, and performance metrics when selecting the most suitable LLM for customer needs analysis tasks. Overall, this study contributes valuable insights for businesses seeking to leverage advanced NLP techniques to enhance customer experience and drive operational efficiency in the travel industry.

Related papers

• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMs [6.179084469089114]
  This paper presents EasyJudge, a model developed to evaluate significant language model responses. It is lightweight, precise, efficient, and user-friendly, featuring an intuitive visualization interface for ease of deployment and use.
  arXiv  Detail & Related papers  (2024-10-13T08:24:12Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Exploring Large Language Models for Feature Selection: A Data-centric Perspective [17.99621520553622]
  Large Language Models (LLMs) have influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. We aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective. Our findings emphasize the effectiveness and robustness of text-based feature selection methods and showcase their potentials using a real-world medical application.
  arXiv  Detail & Related papers  (2024-08-21T22:35:19Z)
• LLM-Select: Feature Selection with Large Language Models [64.5099482021597]
  Large language models (LLMs) are capable of selecting the most predictive features, with performance rivaling the standard tools of data science. Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place.
  arXiv  Detail & Related papers  (2024-07-02T22:23:40Z)
• AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
  Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations. Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
  arXiv  Detail & Related papers  (2024-06-17T04:20:02Z)
• Assessing the Performance of Chinese Open Source Large Language Models in Information Extraction Tasks [12.400599440431188]
  Information Extraction (IE) plays a crucial role in Natural Language Processing (NLP) Recent experiments focusing on English IE tasks have shed light on the challenges faced by Large Language Models (LLMs) in achieving optimal performance.
  arXiv  Detail & Related papers  (2024-06-04T08:00:40Z)
• LLM In-Context Recall is Prompt Dependent [0.0]
  A model's ability to do this significantly influences its practical efficacy and dependability in real-world applications. This study demonstrates that an LLM's recall capability is not only contingent upon the prompt's content but also may be compromised by biases in its training data.
  arXiv  Detail & Related papers  (2024-04-13T01:13:59Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.