22 citations · 108 across the 43 of their papers we have counts for
4 papers · 1 filter
Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has become the primary method for performing natural language tasks with Large Language Models (LLMs). The knowledge acquired during pre-training is cruci…
BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts
Maria-Eleni Zoumpoulidi, Georgios Paraskevopoulos, Alexandros Potamianos
Despite the remarkable capabilities of large language models (LLMs) across a range of tasks, mathematical reasoning remains a challenging frontier. Motivated by the observation tha…
Y-Drop: A Conductance based Dropout for fully connected layers
Efthymios Georgiou, Georgios Paraskevopoulos, Alexandros Potamianos
In this work, we introduce Y-Drop, a regularization method that biases the dropout algorithm towards dropping more important neurons with higher probability. The backbone of our ap…
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) without updating the models' parameters, in cont…