4 papers
JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation
Hadi Nekoei, Aman Jaiswal, Patrice Bechard +7
Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tunin…
Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows
Orlando Marquez Ayala, Patrice Bechard, Emily Chen +2
Large Language Models (LLMs) such as GPT-4o can handle a wide range of complex tasks with the right prompt. As per token costs are reduced, the advantages of fine-tuning Small Lang…
Multi-task retriever fine-tuning for domain-specific and efficient RAG
Patrice Béchard, Orlando Marquez Ayala
Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying Large Language Models (LLMs), as it can address typical limitations such as generating hallucinated or out…
Generating a Low-code Complete Workflow via Task Decomposition and RAG
Orlando Marquez Ayala, Patrice Béchard
AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, images, and video, AI-based systems are increasin…