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Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks

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arxiv 2305.05862 v2 pith:MJLQ6MRT submitted 2023-05-10 cs.CL cs.AI

Xianzhi Li , Samuel Chan , Xiaodan Zhu , Yulong Pei , Zhiqiang Ma , Xiaomo Liu , Sameena Shah This is my paper
classification cs.CLcs.AI
keywords modelsfinancialtasksanalyticalchatgptdomaingpt-4performance
verification ladder T0 review T1 audit T2 compute T3 formal
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The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective are such models in the financial domain? Understanding this basic question would have a significant impact on many downstream financial analytical tasks. In this paper, we conduct an empirical study and provide experimental evidences of their performance on a wide variety of financial text analytical problems, using eight benchmark datasets from five categories of tasks. We report both the strengths and limitations of the current models by comparing them to the state-of-the-art fine-tuned approaches and the recently released domain-specific pretrained models. We hope our study can help understand the capability of the existing models in the financial domain and facilitate further improvements.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation

    cs.CL 2025-06 conditional novelty 6.0 of 10

    ReG refines weak graph-retriever supervision with LLM-selected reasoning chains and reorganizes retrieved triples into coherent evidence chains, improving KGQA accuracy, data efficiency, and reasoning token efficiency.

  2. Rethinking the Understanding Ability across LLMs through Mutual Information

    cs.CL 2025-05 conditional novelty 4.0 of 10

    The paper uses token-level recoverability as a computable lower bound on mutual information to compare LLMs and to fine-tune them, finding encoder-only models preserve information better than decoder-only models.

  3. Assessing the Capabilities and Limitations of FinGPT Model in Financial NLP Applications

    cs.CL 2025-07 reject novelty 3.0 of 10

    FinGPT matches GPT-4 on financial sentiment and headline classification, lags on QA and NER, and shows a bullish bias in stock movement prediction.

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