Financial Text Classification Based On rLoRA Finetuning On Qwen3-8B model

• URL: http://arxiv.org/abs/2512.00630v1
• Date: Sat, 29 Nov 2025 21:04:13 GMT
• Title: Financial Text Classification Based On rLoRA Finetuning On Qwen3-8B model
• Authors: Zhiming Lian,
• Abstract summary: State-of-the-art model Qwen3-8B exhibits strong instruction-following and multilingual capabilities.<n>It is specifically optimized for efficient fine tuning and high performance on reasoning-based benchmarks.<n>The synergy of instruction-based fine-tuning and memory-efficient optimization methods suggests Qwen3-8B can potentially serve as a scalable, economical option for real-time financial NLP applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial text classification has increasingly become an important aspect in quantitative trading systems and related tasks, such as financial sentiment analysis and the classification of financial news. In this paper, we assess the performance of the large language model Qwen3-8B on both tasks. Qwen3-8B is a state-of-the-art model that exhibits strong instruction-following and multilingual capabilities, and is distinct from standard models, primarily because it is specifically optimized for efficient fine tuning and high performance on reasoning-based benchmarks, making it suitable for financial applications. To adapt this model, we apply Noisy Embedding Instruction Finetuning and based on our previous work, this method increases robustness by injecting controlled noise into the embedding layers during supervised adaptation. We improve efficiency further with Rank-stabilized Low-Rank Adaptation low-rank optimization approach, and FlashAttention, which allow for faster training with lower GPU memory. For both tasks, we benchmark Qwen3-8B against standard classical transformer models, such as T5, BERT, and RoBERTa, and large models at scale, such as LLaMA1-7B, LLaMA2-7B, and Baichuan2-7B. The findings reveal that Qwen3-8B consistently surpasses these baselines by obtaining better classification accuracy and needing fewer training epochs. The synergy of instruction-based fine-tuning and memory-efficient optimization methods suggests Qwen3-8B can potentially serve as a scalable, economical option for real-time financial NLP applications. Qwen3-8B provides a very promising base for advancing dynamic quantitative trading systems in the future.

Related papers

• LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models [7.216206616406649]
  Large language models (LLMs) like BloombergGPT and FinMA have set new benchmarks across various financial NLP tasks.<n>We propose Layer-wise Adaptive Ensemble Tuning (LAET), a novel strategy that selectively fine-tunes the most effective layers of pre-trained LLMs.<n>Our approach shows strong results in financial NLP tasks, outperforming existing benchmarks and state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-11-14T13:57:46Z)
• Structured Uncertainty guided Clarification for LLM Agents [126.26213027785813]
  LLM agents extend large language models with tool-calling capabilities, but ambiguous user instructions often lead to incorrect invocations and task failures.<n>We introduce a principled formulation of structured uncertainty over tool-call parameters, modeling joint tool-argument clarification as a POMDP with Expected Value of Perfect Information (EVPI) objective for optimal question selection and aspect-based cost modeling to prevent redundancy.<n>Our SAGE-Agent leverages this structured uncertainty to achieve superior efficiency: increasing coverage on ambiguous tasks by 7-39% while reducing clarification questions by 1.5-2.7$times$ compared to strong prompting and uncertainty-based baselines.
  arXiv  Detail & Related papers  (2025-11-11T21:50:44Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data [12.136710894967088]
  We introduce Type-Compliant Adaptation Cascades, a framework that recasts workflow adaptation as learning typed probabilistic programs.<n> Empirically, TACs significantly outperform state-of-the-art prompt-optimization baselines.
  arXiv  Detail & Related papers  (2025-08-25T17:36:21Z)
• InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities [27.09178257629886]
  InfiAlign is a scalable and sample-efficient post-training framework for large language models (LLMs)<n>At the core of InfiAlign is a robust data selection pipeline that automatically curates high-quality alignment data from open-source reasoning.<n>Our results highlight the effectiveness of combining principled data selection with full-stage post-training.
  arXiv  Detail & Related papers  (2025-08-07T15:34:06Z)
• Shadow-FT: Tuning Instruct Model via Training on Paired Base Model [67.20706292627106]
  Large language models (LLMs) consistently benefit from further fine-tuning on various tasks.<n>We propose a novel Shadow-FT framework to tune the Instruct models by leveraging the corresponding Base models.<n>Our proposed Shadow-FT introduces no additional parameters, is easy to implement, and significantly improves performance.
  arXiv  Detail & Related papers  (2025-05-19T05:16:21Z)
• ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization [51.280919773837645]
  We develop ScoreFlow, a high-performance framework for agent workflow optimization.<n>ScoreFlow incorporates Score-DPO, a novel variant of the direct preference optimization method that accounts for quantitative feedback.<n>It achieves an 8.2% improvement over existing baselines across question answering, coding, and mathematical reasoning.
  arXiv  Detail & Related papers  (2025-02-06T18:47:49Z)
• FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation [13.815905522616838]
  Finetuning large language models (LLMs) has shown remarkable performance in financial tasks.<n>However, finetuning FinLLMs poses challenges including GPU memory constraints and long input sequences.<n>We employ quantized low-rank adaptation (QLoRA) to finetune FinLLMs, which leverage low-rank matrix decomposition and quantization techniques.
  arXiv  Detail & Related papers  (2024-12-16T02:05:49Z)
• A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification [0.8192907805418583]
  Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks. This study investigates the efficacy of instruction fine-tuning to enhance their performance in financial text classification tasks.
  arXiv  Detail & Related papers  (2024-11-04T18:06:36Z)
• Large Continual Instruction Assistant [59.585544987096974]
  Continual Instruction Tuning (CIT) is adopted to instruct Large Models to follow human intent data by data.<n>Existing update gradient would heavily destroy the performance on previous datasets during CIT process.<n>We propose a general continual instruction tuning framework to address the challenge.
  arXiv  Detail & Related papers  (2024-10-08T11:24:59Z)
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning [55.96599486604344]
  We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process. We use Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals. The proposed algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this newly generated step-level preference data.
  arXiv  Detail & Related papers  (2024-05-01T11:10:24Z)
• AutoFT: Learning an Objective for Robust Fine-Tuning [60.641186718253735]
  Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. Current approaches to robust fine-tuning use hand-crafted regularization techniques. We propose AutoFT, a data-driven approach for robust fine-tuning.
  arXiv  Detail & Related papers  (2024-01-18T18:58:49Z)
• PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation [97.78045712375047]
  We present a new efficient model architecture for large language models (LLMs) We show that PanGu-$pi$-7B can achieve a comparable performance to that of benchmarks with about 10% inference speed-up. In addition, we have deployed PanGu-$pi$-7B in the high-value domains of finance and law, developing an LLM named YunShan for practical application.
  arXiv  Detail & Related papers  (2023-12-27T11:49:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.