Data-to-text Generation (D2T) aims to generate textual natural language statements that can
fluently and precisely describe the structured data such as graphs, tables, and meaning …

The disruptive technology provided by large-scale pre-trained language models (LLMs)
such as ChatGPT or GPT-4 has received significant attention in several application domains …

Large language models (LLMs) have demonstrated an impressive ability to synthesize
plausible and fluent text. However they remain vulnerable to hallucinations, and thus their …

Through their transfer learning abilities, highly-parameterized large pre-trained language
models have dominated the NLP landscape for a multitude of downstream language tasks …

Heterogenity of data-to-text generation datasets limits the research on data-to-text
generation systems. We present TabGenie-a toolkit which enables researchers to explore …

The field of Math-NLP has witnessed significant growth in recent years, motivated by the
desire to expand LLM performance to the learning of non-linguistic notions (numerals, and …

Abstract Machine translation has revolutionized the field of language translation in the last
decade. Initially dominated by statistical models, the rise of deep learning techniques has …

Off-the-shelf pre-trained language models have become the de facto standard in NLP
pipelines for a multitude of downstream tasks. However, the inability of these models to …

This thesis presents a domain-driven approach to sports game summarization, a specific
instance of large data-to-text generation (DTG). We first address the data fidelity issue in the …