Zero is Not Hero Yet: Benchmarking Zero-Shot Performance of LLMs for Financial Tasks

Autor:	Shah, Agam, Chava, Sudheer
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Quantitative Finance - General Finance
Druh dokumentu:	Working Paper
Popis:	Recently large language models (LLMs) like ChatGPT have shown impressive performance on many natural language processing tasks with zero-shot. In this paper, we investigate the effectiveness of zero-shot LLMs in the financial domain. We compare the performance of ChatGPT along with some open-source generative LLMs in zero-shot mode with RoBERTa fine-tuned on annotated data. We address three inter-related research questions on data annotation, performance gaps, and the feasibility of employing generative models in the finance domain. Our findings demonstrate that ChatGPT performs well even without labeled data but fine-tuned models generally outperform it. Our research also highlights how annotating with generative models can be time-intensive. Our codebase is publicly available on GitHub under CC BY-NC 4.0 license. Comment: Working Paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.16633 Zobrazit plný text záznamu View this record from Arxiv