most citedFinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MA2025

Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading

Jifeng Li, Arnav Grover, Abraham Alpuerto +2

The financial market is a mission-critical playground for AI agents due to its temporal dynamics and low signal-to-noise ratio. Building an effective algorithmic trading system may…

cs.LG2025

A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction

Subhabrata Das, Bodruzzaman Khan, Xiao-Yang Liu

Accurately forecasting power outages is a complex task influenced by diverse factors such as weather conditions [1], vegetation, wildlife, and load fluctuations. These factors intr…

cs.CE2025

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets

Dannong Wang, Jaisal Patel, Daochen Zha +2

Low-rank adaptation (LoRA) methods show great potential for scaling pre-trained general-purpose Large Language Models (LLMs) to hundreds or thousands of use scenarios. However, the…

quant-ph2025

Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations

Zhiyuan Wang, Chunlin Feng, Christopher Poon +5

Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) er…

cs.LG20252 cited

FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation

Dannong Wang, Daniel Kim, Bo Jin +4

Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval. Due to privacy concerns, finetuni…