mlsanityData to 7 Oct 2026

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

arXiv 2305.16633 ↗First posted 26 May 2023cs.CL q-fin.GNcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

14 of 31 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  6. 2023A Survey of Large Language Modelscited by 2,537
  7. 2023BloombergGPT: A Large Language Model for Financecited by 923
  8. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 486
  9. 2023Augmented Language Models: a Surveycited by 398
  10. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
  11. 2013Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Textscited by 310
  12. 2022WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domaincited by 117
2 more
  1. 2023Trillion Dollar Words: A New Financial Dataset, Task & Market Analysiscited by 73
  2. 2023FiNER-ORD: Financial Named Entity Recognition Open Research Datasetcited by 17

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Citing papers per year: 2023: 2; 2024: 7; 2025: 3; 2026: 2.

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  1. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 228
  2. 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 217
  3. 2024Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactionscited by 43
  4. 2023Integration of Large Language Models and Federated Learningcited by 28
  5. 2025Plutus: Benchmarking Large Language Models in Low-Resource Greek Financecited by 16
  6. 2024LLaMandement: Large Language Models for Summarization of French Legislative Proposalscited by 9
  7. 2024SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysiscited by 8
  8. 2025Beyond the Reported Cutoff: Where Large Language Models Fall Short on Financial Knowledgecited by 8
  9. 2024Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Modelcited by 6
  10. 2024Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Modelscited by 5
  11. 2025Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinementcited by 4
  12. 2024Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigmscited by 1
2 more
  1. 2026Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning?cited by 0
  2. 2026CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecastingcited by 0