LAMIA : An LLM Approach for Task-Oriented Dialogue Systems in Industry 5.0 (2025)

Abstract: Human-Machine Interaction (HMI) plays an important role in Industry 5.0, improving worker well-being by automating repetitive tasks and enhancing seamless collaboration between humans and intelligent systems. In this context, Task-Oriented Dialogue (TOD) systems are a commonly used approach to enable natural communication in these settings, traditionally developed using rule-based approaches. However, the revolution of Large Language Models (LLMs) is changing how dialogue systems are being developed without the necessity of relying on tedious and rigid handcrafted rules. Despite their popularity, their application in industrial contexts remains underexplored, necessitating a solution to challenges such as hallucinations, lack of domain-specific data, high training costs, and limited adaptability. In order to explore the contribution of LLMs in the industry field, this work presents LAMIA, a task-oriented dialogue system for industrial scenarios that leverages LLMs through prompt tuning. This system has been adapted and evaluated for a bin-picking use case, using GPT-3.5 Turbo, showing to be an intuitive method for new use cases in Industry 5.0.

## introduction industry 5.0 focuses on human workers and their well-being at the centre of the productive process. in this context, human-machine interaction (hmi) interfaces are an important asset that allow communication between humans and machines @xcite . this simpler way of interaction, by allowing, for example, the automation of repetitive tasks, improves task efficiency and user experience @xcite , and allows workers to focus on more creative tasks @xcite . in this setting, large language models (llms) have emerged as powerful tools, enabling more intuitive interactions via applications like virtual assistants and conversational agents, making technology more accessible to a wider audience. llms have significant potential in industry 5.0, particularly through their role in task-oriented dialogue (tod) systems, which enable a natural hmi aimed at facilitating problem-solving tasks within specific domains @xcite . however, the use of llms in industrial scenarios is not as widespread as in other fields, as these models still face challenges, relevant in those scenarios that admit little or no margin of error, such as production processes. these limitations are hallucinations foot_0 , lack of domain-specific data, and the difficulty and high costs associated with training for new applications. to address these limitations, this article explores the contribution and role of llms in the development of tod systems in industrial environments. to do this, the use of prompt tuning is explored, as it allows models to be adapted using strategies such as few-shot learning or step-by-step thinking @xcite by teaching the model how to behave with prompts in natural language. the research has led to the development of lamia (large language models for industrial assistance), a tod system designed for industrial environments. optimized via prompt tuning, lamia reduces hallucinations and the need for large amounts of data, enhances adaptability, and mitigates high computational costs. having been implemented with the llm gpt-3.5 turbo, lamia presents a cost-effective solution for seamless human-machine interaction in industry 5.0. according to the designs used for the development of tod systems, pipelines typically follow either a modular approach or an end-to-end approach. the modular approach consists of separate components for natural language understanding (nlu), dialogue state tracking (dst), dialogue policy learning (dp), and natural language generation (nlg) @xcite . in contrast, the end-to-end approach, introduced by @xcite , creates a trainable end-to-end model that still connects in a modularized way, but does not directly modularize the user goal. over the years, the main strategies used to develop these systems were rule-based methods or the use of annotated data @xcite . however, both present challenges in industry 5.0, as rule-based methods require extensive manual adaptation, while data-driven approaches suffer from a lack of domain-specific data in industrial settings @xcite and high computational demands. ## approach and implementation to address the challenges in building tod systems for industry 5.0, we have developed lamia, a tod system for industry implemented through prompt tuning. among its capabilities, lamia allows operators to maintain natural language conversations and send the final task to the target system. the research was conducted at tekniker, a technology research centre in the basque country, where we had access to kide4i @xcite . kide4i is a rule-based tod system for industry 5.0 composed of four modules (key element extraction, polarity interpreter, semantic repository and dialogue manager) which rely on syntactic and morphological parsers, and ontologies for storing domain knowledge and managing the dialogue process. more specifically, its use case of a bin-picking robot and its evaluation framework served as our reference to assess the performance of lamia in a real-world scenario. ## evaluation to evaluate lamia, we utilized the kide4i's evaluation framework from @xcite . the lamia system has been evaluated by comparing its performance with the kide4i standard to assess whether lamia offers improvements over traditional rule-based systems. the evaluations consist of two key components: furthermore, lamia llm-based modules (nlu, polarity interpreter, and nlg) have also been evaluated against the corresponding modules in kide4i. the modules of both systems feature similar functionalities. the nlu module in lamia aligns with kide4i's key element extraction (kee) module, and both incorporate a polarity interpreter module. however, the nlg module in lamia, which is responsible for text generation, does not have a direct counterpart in kide4i, but this module has also been analysed, as it is developed with an llm. lamia's adaptability and economic costs have also been assessed. to follow these evaluations, we have used the same dialogue battery used to assess the kide4i system in @xcite 's work, composed of 75 dialogues. ## conclusions this study offers new insights into the application of large language models (llms) in the development of applications for industry 5.0. the research is focused on exploring the contribution of these models in applications for human-machine interaction (hmi) such as task-oriented dialogue (tod) systems. with this objective, this article presents lamia, a prompt-optimized llm-based tod system for industry 5.0. this system also searches for solving the most criticized limitations present in traditional and llm-based applications, such as difficult adaptability to new use cases and domains due to handcrafted rules, llms' hallucinations, lack of domain-specific data, and the difficulty and high costs associated with re-training these models for new use cases and domains. for this, lamia leverages prompt tuning strategies, which have shown significant advantages in intuitive development, adaptability to new domains, and use cases with low computational costs. in addition, the system has performed better than traditional systems, demonstrating that it is efficient for use in a real industrial setting by being able to complete the task and maintain a smooth dialogue. moreover, this study also reveals the specific contribution of llms and the prompt tuning strategy in this kind of system. the end-to-end approach test showed that prompt tuning does not perform as well with complex tasks or several tasks at once, as already demonstrated by @xcite , which is the reason for using a modular pipeline. however, preliminary experiments in these modules also showed that the effectiveness of llm varies by task, performing almost perfect for nlp tasks, such as generation, classification, or slot filling, and not being the most suitable option for those that manage structured formats. therefore, the contribution and adaptation of llms in tod systems must be consistent and adapted to the purposes of these models, which are natural language understanding, processing, and generation. future research should focus on addressing the main limitation of lamia, the lack of synonymity in the database, which is the main cause for the presence of uncompleted dialogues or the increase of dialogue completion steps. the integration of ontologies as a database could help mitigate this issue by expanding the range of recognized terms, and, therefore, improving the fluidity of the conversation with the system, reducing overall times, and thus reducing costs. furthermore, further investigation of the adaptability of the system is needed, as the synonymity problem could not be present in other use cases, with a real assessment of its scalability looking for out-of-scope problems. an interface deployment for industrial uses is also necessary, with a user study to assess operator's experience. moreover, the use of a proprietary llm can be a handicap for most industries, as it involves a cost. for this reason, the implementation of lamia with an open-source model must also be considered and evaluated. finally, ethical considerations, such as data privacy and transparency, should also be addressed as the system moves toward production use.