large language models

Can LLMs Substitute for Humans in Evaluating Conceptual Modeling Language Vocabularies? An Experimental Study

Effective model-driven engineering of software-intensive systems relies on appropriately designed conceptual modeling languages. A well-designed modeling language includes a set of concepts that users of the language find to be relevant for describing phenomena of interest in a domain, as well as a vocabulary of lexica...

Software Development using Low-Code Platforms or AI-Assistants: A Case Study-based Comparison

The results suggest that LCPs with integrated AI enhance development efficiency and predictability compared to traditional LCPs and purely AI-assisted development.

Modular Meta-Languages for Structured Instructions: A Novel Approach for LLM Integration into Evolving Engineering Toolchains

Large Language Models are increasingly used to generate structured engineering artifacts, yet the instruction artifacts that govern this generation are rarely treated as modeling artifacts in their own right. They typically appear as monolithic prompt blocks, schemas, or informal examples. When tools, metamodels, APIs,...

From Unfinished to Done: Bridging the MDE Implementation Gap with Constrained LLMs

Model-driven engineering (MDE) excels at ensuring architectural consistency and managing complexity, yet extending generated code skeletons with project-specific business logic often remains a time-consuming manual task. Conversely, large language models (LLMs) offer immense flexibility in coding but may produce vibe-c...

Well-Formed Executable Suggestions for Continuous Model-Driven Engineering

Among the assistance systems proposed in Model-Driven Engineering (MDE), recommender systems continuously monitor modeling activities and dynamically provide modeling suggestions to engineers. The recent emergence of Large Language Models (LLMs) has led to the integration of these artificial intelligence components int...

Model-Driven Engineering for FHIR Testing: Experiences from Australia's Sparked Accelerator

Fast Healthcare Interoperability Resources (FHIR) is a rapidly emerging and inherently model-driven technology in medical informatics. Its complexity makes it a prime candidate for Model-Driven Engineering (MDE). This paper reports on our experience applying progressively richer MDE techniques to FHIR testing within Au...

A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design

The design of reinforced concrete (RC) highway barriers is a safety-critical engineering task that requires strict compliance with regulatory provisions such as the AASHTO LRFD Bridge Design Specifications. Current engineering practice relies largely on manual, iterative, and experience-driven procedures to satisfy com...

MiniMaestro: Resource-Conscious Conversational Music Recommendation with a Single Open-Weight 8B Model

The RecSys Challenge 2026 Music-CRS (TalkPlay) task formalizes this as two coupled sub-problems: given dialogue history and user context, retrieve a ranked list of the top-20 tracks from the full, unrestricted catalog, and generate a response that justifies the recommendation while sustaining conversational coherence.

Overview and Analysis of the RecSys Challenge 2026: Conversational Music Recommendation

This paper presents the RecSys Challenge 2026 challenge task, dataset, evaluation protocol, and official results, and identifies limitations of the benchmark and evaluation protocol, including single-ground-truth relevance and teacher-forced evaluation of synthetic dialogues.

Talk Less, Rank Better: A Decoupled Pipeline for Conversational Music Recommendation

This paper describes the solution submitted by team Hallucinated to the ACM RecSys Challenge 2026, based on the TalkPlayData conversational music recommendation dataset, which addresses the ranking task with a classic multi-stage pipeline that combines a diverse pool of candidate generators, Reciprocal Rank Fusion, and...

Dialogue-Aware Conversational Music Recommendation via Multi-Modal Retrieval and Weighted RRF

The ACM-RecSys 2026 Music Conversational Recommendation Challenge (Music-CRS) bridges natural language processing and recommender systems, requiring systems to engage in multi-turn dialogues and recommend relevant tracks from a large catalog. In this paper, we present Team Sargam’s solution, a robust hybrid retrieval a...

Structured energy news sentiment extraction with Large Language Models for crude oil price forecasting