From the 1 of 4 linked papers with an AI index.
4 papers
Overcoming the Modality Gap in Context-Aided Forecasting
Vincent Zhihao Zheng, Ãtienne Marcotte, Arjun Ashok +4
The paper introduces a semi‑synthetic data augmentation technique to create high‑quality contextual information for time‑series forecasting, producing a 7 million‑sample dataset (C…
Dr-CiK: A Testbed for Foresight-Driven Agents
Yihong Tang, Andrew Robert Williams, Arjun Ashok +6
Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively discovered from noisy, heteroge…
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
Joao Monteiro, Pierre-Andre Noel, Etienne Marcotte +6
Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includes encyclopedic documents that harbor a…
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
João Monteiro, Ãtienne Marcotte, Pierre-André Noël +5
In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference information. Just-in-time processing of a context…