5 papers
Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations
Devika Prasad, Luke Gerschwitz, Tong Li +5
Prompt engineering is crucial for effective interaction with generative artificial intelligence systems, yet existing optimisation methods often operate over an unstructured and va…
ConCISE: A Reference-Free Conciseness Evaluation Metric for LLM-Generated Answers
Seyed Mohssen Ghafari, Ronny Kol, Juan C. Quiroz +5
Large language models (LLMs) frequently generate responses that are lengthy and verbose, filled with redundant or unnecessary details. This diminishes clarity and user satisfaction…
A Robust and Efficient Pipeline for Enterprise-Level Large-Scale Entity Resolution
Sandeepa Kannangara, Arman Abrahamyan, Daniel Elias +5
Entity resolution (ER) remains a significant challenge in data management, especially when dealing with large datasets. This paper introduces MERAI (Massive Entity Resolution using…
FeatureCuts: Feature Selection for Large Data by Optimizing the Cutoff
Andy Hu, Devika Prasad, Luiz Pizzato +5
In machine learning, the process of feature selection involves finding a reduced subset of features that captures most of the information required to train an accurate and efficien…
Do LLM Personas Dream of Bull Markets? Comparing Human and AI Investment Strategies Through the Lens of the Five-Factor Model
Harris Borman, Anna Leontjeva, Luiz Pizzato +2
Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavio…