·Title: Harnessing collaborative intelligence in the Post-LLM world

Abstract: Collaborative data science methods help harness a collective intelligence from fragmented and siloed organizational assets or device eco-systems. The following are two major challenges that arise in unlocking collaborative intelligence: 1.) Maintaining a high resource-efficiency and 2.) enabling collaborative data acquisition . To that extent, I share my research on extremely resource-efficient LLM fine-tuning in the federated and siloed settings. I then provide methods for responsible acquisition of representations (embeddings) of data from multiple entities. This is as opposed to a lot of good work that has been previously done instead on model-sharing in the pre-LLM era. Another problem that I tackle along this path, are methods that post hoc convert a machine learning pipeline trained with informal (heuristic) assurances to those equipped with formal trustworthiness guarantees for representation release. I conclude with my research on the development of data markets, which are pivotal in enabling seamless data acquisition across siloed clients based on efficient federated optimization and optimal experimental design