Investing in Performance: Fine-tune small models with LLM insights - a CFM case study

Optimizing your LLM in production

Rocket Money x Hugging Face: Scaling Volatile ML Models in Production​

Judge Arena: Benchmarking LLMs as Evaluators

BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation

arXiv:2601. 06401v2 Announce Type: replace Abstract: Large language models are becoming increasingly significant in financial applications.

PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management

arXiv:2605. 27887v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM), a critical financial decision-making task, remains poorly benchmarked.

A Theory of Training Profit-Optimal LLMs

arXiv:2605. 16430v2 Announce Type: replace-cross Abstract: Scaling LLMs requires tremendous computational resources, and recent advances in AI have gone hand in hand with massive amounts of capital expenditure.

How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions

arXiv:2606. 08051v1 Announce Type: new Abstract: Financial transaction processing requires extracting structured merchant information from noisy, abbreviated bank transaction strings at scale.

Model Distillation in the API

Fine-tune a cost-efficient model with the outputs of a large frontier model–all on the OpenAI platform

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents

arXiv:2608. 12342v1 Announce Type: cross Abstract: Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making.

Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems

arXiv:2606. 08285v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling.

Director of Machine Learning Insights [Part 3: Finance Edition]

Evaluation of LLMs for Mathematical Formalization in Lean

arXiv:2606. 05632v1 Announce Type: new Abstract: Within the past few years, the ability of Large Language Models (LLMs) to generate formal mathematical proofs has improved drastically.