2 citations · 3 across the 4 of their papers we have counts for
5 papers
Training-Free Continual Learning for Multimodal Forecasting via Prompt Evolution
Laurent Mombaerts, Jonathan Taws, Jacopo Gargano +4
Adapting a forecasting system to a changing world classically means retraining. We ask whether the task of continually learning regimes for time series forecasting can be represent…
Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings
Anirudh Nair, Adi Banerjee, Laurent Mombaerts +2
Prompt engineering represents a critical bottleneck to harness the full potential of Large Language Models (LLMs) for solving complex tasks, as it requires specialized expertise, s…
VERA: Validation and Evaluation of Retrieval-Augmented Systems
Tianyu Ding, Adi Banerjee, Laurent Mombaerts +3
The increasing use of Retrieval-Augmented Generation (RAG) systems in various applications necessitates stringent protocols to ensure RAG systems accuracy, safety, and alignment wi…
Meta Knowledge for Retrieval Augmented Large Language Models
Laurent Mombaerts, Terry Ding, Adi Banerjee +3
Retrieval Augmented Generation (RAG) is a technique used to augment Large Language Models (LLMs) with contextually relevant, time-critical, or domain-specific information without a…
Autoencoder-based General Purpose Representation Learning for Customer Embedding
Jan Henrik Bertrand, David B. Hoffmann, Jacopo Pio Gargano +2
Recent advances in representation learning have successfully leveraged the underlying domain-specific structure of data across various fields. However, representing diverse and com…