9 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…
Computing 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…
A QUBO Framework for Team Formation
Karan Vombatkere, Evimaria Terzi, Theodoros Lappas
The team formation problem assumes a set of experts and a task, where each expert has a set of skills and the task requires some skills. The objective is to find a set of experts t…
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…
FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
Christos Fragkathoulas, Vasiliki Papanikou, Evaggelia Pitoura +1
Counterfactual explanations assess unfairness by revealing how inputs must change to achieve a desired outcome. This paper introduces the first graph-based framework for generating…