Lune

EMNLP Findings2023

Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing

Yue Guo, Zian Xu, Yi Yang

Abstract

The emergence of Large Language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial Language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of encoder-only language models and the decoderonly language models. Our findings reveal that while some decoder-only LLMs demonstrate notable performance across most financial tasks via zero-shot prompting, they generally lag behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study provides foundation evaluations for continuing efforts to build more advanced LLMs in the financial domain.

Ask Lune

Ask anything. The answer quotes the paper.

Lune reads all of it and the papers around it, then shows you the passages behind every answer.

Questions to start from

Lune answers from

the full paper · 2 top-tier papers that cite it · related work

Get the answer

Free to start. No credit card required.

Cited by top-tier papers2

  1. Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A SurveyJiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou et al.ACL 2024 · 15 citations
  2. LLM-Powered Multi-Agent Collaboration for Intelligent Industrial On-Call AutomationRuowei Fu, Yang Zhang, Zeyu Che, Xin Wu et al.ASE 2025

Builds on5

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
  2. Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida et al.NeurIPS 2022 · 24,707 citations
  3. Self-Instruct: Aligning Language Models with Self-Generated InstructionsYizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu et al.ACL 2023 · 540 citations
  4. When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial DomainRaj Sanjay Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah et al.EMNLP 2022 · 63 citations
  5. Trillion Dollar Words: A New Financial Dataset, Task & Market AnalysisAgam Shah, Suvan Paturi, Sudheer ChavaACL 2023 · 27 citations

Related papers

  1. Financial Language Model Evaluation (FLaME)Glenn Matlin, Mika Okamoto, Huzaifa Pardawala, Yang Yang et al.ACL Findings 2025 · 3 citations
  2. FinTral: A Family of GPT-4 Level Multimodal Financial Large Language ModelsGagan Bhatia, El Moatez Billah Nagoudi, Hasan Cavusoglu, Muhammad Abdul-MageedACL Findings 2024 · 37 citations
  3. FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language ModelsJunzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin et al.ACL Findings 2026
  4. Exploring the Impact of Corpus Diversity on Financial Pretrained Language ModelsJaeyoung Choe, Keonwoong Noh, Nayeon Kim, Seyun Ahn et al.EMNLP Findings 2023 · 6 citations
  5. BizBench: A Quantitative Reasoning Benchmark for Business and FinanceMichael Krumdick, Rik Koncel-Kedziorski, Viet Dac Lai, Varshini Reddy et al.ACL 2024 · 10 citations
Dusk over the sea between two cliffs drawn in fine vertical lines