42 citations · 174 across the 28 of their papers we have counts for
35 papers
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective
Bishwamittra Ghosh, Soumi Das, Till Speicher +5
Large language models (LLMs) operate in two fundamental learning modes - fine-tuning (FT) and in-context learning (ICL) - raising key questions about which mode yields greater lang…
Testing the Limits of Truth Directions in LLMs
Angelos Poulis, Mark Crovella, Evimaria Terzi
Large language models (LLMs) have been shown to encode truth of statements in their activation space along a linear truth direction. Previous studies have argued that these directi…
Approximate Pareto Frontiers for Submodular Utility and Cost Tradeoffs
Karan Vombatkere, Evimaria Terzi
In many data-mining applications, including recommender systems, influence maximization, and team formation, the goal is to pick a subset of elements (e.g., items, nodes in a netwo…
Online Two-Stage Submodular Maximization
Iasonas Nikolaou, Miltiadis Stouras, Stratis Ioannidis +1
Given a collection of monotone submodular functions, the goal of Two-Stage Submodular Maximization (2SSM) [Balkanski et al., 2016] is to restrict the ground set so an objective sel…
Understanding the Interplay between Memorization and Learning in Large Language Models
Bishwamittra Ghosh, Soumi Das, Qinyuan Wu +4
We investigate foundational questions about the interplay between memorization and learning in large language models (LLMs). When an LLM generates a string, can we disentangle the…
Forming Coordinated Teams that Balance Task Coverage and Expert Workload
Karan Vombatkere, Evimaria Terzi, Aristides Gionis
We study a new formulation of the team-formation problem, where the goal is to form teams to work on a given set of tasks requiring different skills. Deviating from the classic pro…