mlsanityData to 14 Oct 2026

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard

arXiv 2504.13125 ↗First posted 17 Apr 2025cs.CL cs.AI cs.LGcited by 2

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Abstract (arXiv)

This paper investigates the application of large language models (LLMs) to financial tasks. We fine-tuned foundation models using the Open FinLLM Leaderboard as a benchmark. Building on Qwen2.5 and Deepseek-R1, we employed techniques including supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL) to enhance their financial capabilities. The fine-tuned models demonstrated substantial performance gains across a wide range of financial tasks. Moreover, we measured the data scaling law in the financial domain. Our work demonstrates the potential of large language models (LLMs) in financial applications.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 17 Apr 2025.

Builds on

11 of 17 references.

References · 11

  1. 2020Scaling Laws for Neural Language Modelscited by 6,586
  2. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  3. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  4. 2023DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Modelscited by 245
  5. 2023EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activitiescited by 154
  6. 2024FinBen: A Holistic Financial Benchmark for Large Language Modelscited by 141
  7. 2024Learning Dynamics of LLM Finetuningcited by 95
  8. 2024Simulating Financial Market via Large Language Model based Agentscited by 25
  9. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  10. 2025Phase Transitions in Large Language Models and the O(N) Modelcited by 12
  11. 2024A Report on Financial Regulations Challenge at COLING 2025cited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuningcited by 0
  2. 2026FMplex: Model Virtualization for Serving Extensible Foundation Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance
  2. 2025Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
  3. 2023DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
  4. 2024SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
  5. 2025Demystifying Domain-adaptive Post-training for Financial LLMs
  6. 2025Technical Report: Full-Stack Fine-Tuning for the Q Programming Language
  7. 2026QuantEval: A Benchmark for Financial Quantitative Tasks in Large Language Models
  8. 2024Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications