mlsanityData to 7 Oct 2026

Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs

arXiv 2407.17624 ↗First posted 24 Jul 2024q-fin.RM cs.CL q-fin.GNcited by 5

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Abstract (arXiv)

Large Language Models (LLMs) have been shown to perform well for many downstream tasks. Transfer learning can enable LLMs to acquire skills that were not targeted during pre-training. In financial contexts, LLMs can sometimes beat well-established benchmarks. This paper investigates how well LLMs perform in the task of forecasting corporate credit ratings. We show that while LLMs are very good at encoding textual information, traditional methods are still very competitive when it comes to encoding numeric and multimodal data. For our task, current LLMs perform worse than a more traditional XGBoost architecture that combines fundamental and macroeconomic data with high-density text-based embedding features.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 40 of its 109 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

40 of 109 references.

References · 40

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026RealFin: How Well Do LLMs Reason About Finance When Users Leave Things Unsaid?cited by 9
  2. 2026FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learningcited by 4
  3. 2025When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Taskscited by 2
  4. 2026Fair outputs, Biased Internals: Causal Potency and Asymmetry of Latent Bias in LLMs for High-Stakes Decisionscited by 1
  5. 2026The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysiscited by 0