Paper: arXiv 2407.01953

Abstract

The integration of Large Language Models (LLMs) into financial analysis has garnered significant attention in the NLP community. This paper presents our solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs within three critical areas of financial tasks: financial classification, financial text summarization, and single stock trading. We adopted Llama3-8B and Mistral-7B as base models, fine-tuning them through Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) approaches. To enhance model performance, we combine datasets from task 1 and task 2 for data fusion. Our approach aims to tackle these diverse tasks in a comprehensive and integrated manner, showcasing LLMs’ capacity to address diverse and complex financial tasks with improved accuracy and decision-making capabilities.

Complexity vs Empirical Score

  • Math Complexity: 1.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper is heavily implementation-focused, detailing specific model architectures (Llama3-8B, Mistral-7B), parameter-efficient fine-tuning (PEFT/LoRA), hardware specifications (RTX-A6000 GPUs), and data fusion strategies with clear validation metrics; however, it contains almost no advanced mathematical derivations or theoretical proofs, relying instead on empirical testing.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Evaluate LLMs in Financial Analysis"] --> B{"Base Models:<br>Llama3-8B & Mistral-7B"}
  B --> C{"Data Fusion Strategy"}
  C --> D["Parameter Efficient<br>Fine-Tuning PEFT"]
  D --> E["Low-Rank<br>Adaptation LoRA"]
  E --> F["Task Execution:<br>Classify, Summarize, Trade"]
  F --> G["Outcomes:<br>Enhanced Accuracy &<br>Financial Decision Making"]

Cited References (17)

  1. A survey of fintech research and policy discussion (2021)
  2. Natural language processing in accounting, auditing and finance: A synthesis of the literature with a roadmap for future research (2016)
  3. The FinBen: An Holistic Financial Benchmark for Large Language Models (2024) — link
  4. Large language models in finance: A survey (2023)
  5. FinMem: A performance-enhanced LLM trading agent with layered memory and character design (2024)
  6. Sparks of artificial general intelligence: Early experiments with gpt-4 (2023) — link
  7. Fine-grained argument understanding with bert ensemble techniques: A deep dive into financial sentiment analysis (2023)
  8. PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods (2022)
and 9 more
  1. Mistral 7B (2023) — link
  2. Trade the event: Corporate events detection for news-based event-driven trading (2021) — link
  3. Gpt-4 technical report (2023) — link
  4. Llama 3 Model Card (2024) — link
  5. Gemini: a family of highly capable multimodal models (2023) — link
  6. Lora: Low-rank adaptation of large language models (2021) — link
  7. Quantization and training of neural networks for efficient integer-arithmetic-only inference (2018)
  8. An information fusion based approach to context-based fine-tuning of GPT models (2024)
  9. Multi-view fusion for instruction mining of large language model (2024)

0 of 17 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.