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Publications about AI methods and applications.
Research paper by Hicham Assoudi. arXiv record dated 2025-05-05. Research area: Language technology.
Research paper by Alejandro Carmona-Martínez and collaborators, published 2026 in Machine Learning and Knowledge Extraction. Research area: Topic Modeling.
Cyber threats are constantly evolving. Extracting actionable insights from unstructured Cyber Threat Intelligence (CTI) data is essential to guide cybersecurity decisions. Increasingly, organizations like Microsoft, Trend Micro, and CrowdStrike are using generative AI to facilitate CTI extraction. This paper addresses the challenge of automating the extraction of actionable CTI using advancements in Large Language Models (LLMs) and Knowledge Graphs (KGs). We explore the application of state-of-the-art open-source LLMs, including the Llama 2 series, Mistral 7B Instruct, and Zephyr for extracting meaningful triples from CTI texts. Our methodolo
Research paper by Shaltiel Shmidman and collaborators. Published 2024-07-09. Topics: LLMs, DictaLM2.0, DictaLM2.0-Instruct, Mistral model, pre-trained model.
In this paper, we explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance automated design and software development in the automotive industry. We present two case studies: a standardization compliance chatbot and a design copilot, both utilizing RAG to provide accurate, context-aware responses. We evaluate four LLMs-GPT-4o, LLAMA3, Mistral, and Mixtral -- comparing their answering accuracy and execution time. Our results demonstrate that while GPT-4 offers superior performance, LLAMA3 and Mistral also show promising capabilities for local deployment, addressing data privacy concerns in aut
Research paper by Lifan Yuan and collaborators. Published 2024-04-02. Topics: large language models (LLMs), Mistral-7B, CodeLlama-70B, reasoning, fine-tuning.
Research paper by Arindam Mitra and collaborators. Published 2024-07-03. Topics: AgentInstruct, generative teaching, language models, post-training, prompts.
Large Language Models (LLMs) have become the foundation of modern Artificial Intelligence systems, enabling breakthroughs in natural language understanding, reasoning, code generation, multimodal learning, and autonomous agents. Recent advances in Transformer-based architectures have significantly improved model capabilities, scalability, and generalization performance. This paper presents a comprehensive analytical review of modern LLM architectures, tracing their evolution from early neural language models to contemporary frontier systems such as GPT, Claude, Gemini, LLaMA, DeepSeek, and Mistral. The study examines core architectural compon
We introduce ADBL2, an assisted debate builder tool. It is based on the capability of large language models to generalise and perform relation-based argument mining in a wide-variety of domains. It is the first open-source tool that leverages relation-based mining for (1) the verification of pre-established relations in a debate and (2) the assisted creation of new arguments by means of large language models. ADBL2 is highly modular and can work with any open-source large language models that are used as plugins. As a by-product, we also provide the first fine-tuned Mistral-7B large language model for relation-based argument mining, usable by
Research paper by Hasan Eker and collaborators, published 2026 in Applied Sciences. Research area: Topic Modeling.
Research paper by Yifan Zhang and collaborators. Published 2024-02-12. Topics: meta-prompted language models, zero-shot verifiers, continual pretraining, AutoMathText dataset, Mistral language model.
The automatic generation of hints by Large Language Models (LLMs) within Intelligent Tutoring Systems (ITSs) has shown potential to enhance student learning. However, generating pedagogically sound hints that address student misconceptions and adhere to specific educational objectives remains challenging. This work explores using LLMs (GPT-4o and Llama-3-8B-instruct) as teachers to generate effective hints for students simulated through LLMs (GPT-3.5-turbo, Llama-3-8B-Instruct, or Mistral-7B-instruct-v0.3) tackling math exercises designed for human high-school students, and designed using cognitive science principles. We present here the stud
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