Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

We propose the use of conversational GPT models for easy and quick few-shot text
classification in the financial domain using the Banking77 dataset. Our approach involves in …

Quantitative data is important in many domains. Information extraction methods draw
structured data from documents. However, the extraction of quantities and their contexts has …

Natural language processing for finance has gained significant attention from both
academia and the industry as the continuously increasing amount of financial texts has …

Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is
impractical in data-limited domains. Few-shot methods offer an alternative, utilizing …

This paper provides an overview of FinArg-1 shared tasks in NTCIR-17. We propose six
subtasks with three different resources, including company manager presentations …

As large language models (LLMs) impact a growing number of complex domains, it is
becoming increasingly important to have fair, accurate, and rigorous evaluation …

Identifying named entities such as a person, location or organization, in documents can
highlight key information to readers. Training Named Entity Recognition (NER) models …

Large Language Models (LLMs) have shown remarkable capabilities across a wide variety
of Natural Language Processing (NLP) tasks and have attracted attention from multiple …

Numbers are crucial for various real-world domains such as finance, economics, and
science. Thus, understanding and reasoning with numbers are essential skills for language …