1 citations · 2 across the 5 of their papers we have counts for
5 papers
Instruction Finetuning for Leaderboard Generation from Empirical AI Research
Salomon Kabongo, Jennifer D'Souza
This study demonstrates the application of instruction finetuning of pretrained Large Language Models (LLMs) to automate the generation of AI research leaderboards, extracting (Tas…
Exploring the Latest LLMs for Leaderboard Extraction
Salomon Kabongo, Jennifer D'Souza, Sören Auer
The rapid advancements in Large Language Models (LLMs) have opened new avenues for automating complex tasks in AI research. This paper investigates the efficacy of different LLMs-M…
Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study
Salomon Kabongo, Jennifer D'Souza, Sören Auer
This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating Artificial Intelligence (AI) research leaderboards, a task defin…
Zero-shot Entailment of Leaderboards for Empirical AI Research
Salomon Kabongo, Jennifer D'Souza, Sören Auer
We present a large-scale empirical investigation of the zero-shot learning phenomena in a specific recognizing textual entailment (RTE) task category, i.e. the automated mining of…
BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus
Josh Meyer, David Ifeoluwa Adelani, Edresson Casanova +16
BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single sp…