1 citations · 1 across the 9 of their papers we have counts for
10 papers
Know Yourself and Know Your Neighbour: A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork
Vasudevan Nedumpozhimana, John Kelleher
Sentence representation learning is still an open challenge in Natural Language Processing. In this work, we propose a new self-supervised framework for learning sentence represent…
Analysing the Linearity of Linguistic Relations in Language Model Embedding Spaces
Vasudevan Nedumpozhimana, Fathima Thekkekara, John Kelleher
We propose a framework to analyse how strongly different linguistic relations are linearly encoded in language model embedding spaces. We formalise linear encoding via a constraine…
The Nuts and Bolts of Natural Language to SQL Translation: A Systematic Analysis of Model Pipeline Optimisation Approaches and their Interactions
Filip Klubicka, Vasudevan Nedumpozhimana, Sneha Rautmare +3
In the age of large language models, Natural Language to SQL (NL2SQL) translation remains an open problem with many useful applications. We explore interactions between several NL2…
Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving
Yasmin Moslem, Magdalena Kacmajor, Vasudevan Nedumpozhimana +11
Efficient deployment of large language models (LLMs) in production forces a trade-off between accuracy and cost. Operators often default to a single model that is either expensive…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
Topic Aware Probing: From Sentence Length Prediction to Idiom Identification how reliant are Neural Language Models on Topic?
Vasudevan Nedumpozhimana, John D. Kelleher
Transformer-based Neural Language Models achieve state-of-the-art performance on various natural language processing tasks. However, an open question is the extent to which these m…
Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space
Filip Klubička, Vasudevan Nedumpozhimana, John D. Kelleher
The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method. We repurpose an existing English v…
Embedding Cyclical Information in Solar Irradiance Forecasting
T. A. Fathima, Vasudevan Nedumpozhimana, Yee Hui Lee +1
In this paper, we demonstrate the importance of embedding temporal information for an accurate prediction of solar irradiance. We have used two sets of models for forecasting solar…
Predicting Solar Irradiance in Singapore
T. A. Fathima, Vasudevan Nedumpozhimana, Yee Hui Lee +2
Solar irradiance is the primary input for all solar energy generation systems. The amount of available solar radiation over time under the local weather conditions helps to decide…
A Chaotic Approach on Solar Irradiance Forecasting
T. A. Fathima, Vasudevan Nedumpozhimana, Yee Hui Lee +2
We analyse the time series of solar irradiance measurements using chaos theory. The False Nearest Neighbour method (FNN), one of the most common methods of chaotic analysis is used…