Financial Text Analysis

Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination ArXiv ID: 2311.15548 “View on arXiv” Authors: Unknown Abstract The hallucination issue is recognized as a fundamental deficiency of large language models (LLMs), especially when applied to fields such as finance, education, and law. Despite the growing concerns, there has been a lack of empirical investigation. In this paper, we provide an empirical examination of LLMs’ hallucination behaviors in financial tasks. First, we empirically investigate LLM model’s ability of explaining financial concepts and terminologies. Second, we assess LLM models’ capacity of querying historical stock prices. Third, to alleviate the hallucination issue, we evaluate the efficacy of four practical methods, including few-shot learning, Decoding by Contrasting Layers (DoLa), the Retrieval Augmentation Generation (RAG) method and the prompt-based tool learning method for a function to generate a query command. Finally, our major finding is that off-the-shelf LLMs experience serious hallucination behaviors in financial tasks. Therefore, there is an urgent need to call for research efforts in mitigating LLMs’ hallucination. ...

FinBERT - A Large Language Model for Extracting Information from Financial Text ArXiv ID: ssrn-3910214 “View on arXiv” Authors: Unknown Abstract We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can bette Keywords: FinBERT, Natural Language Processing, Large Language Models, Financial Text Analysis, Technology/AI Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper focuses on fine-tuning a pre-existing transformer model (FinBERT) with specific financial datasets, which is primarily an empirical, implementation-heavy task with significant data preparation and evaluation metrics, while the underlying mathematics is standard deep learning rather than novel or dense derivations. flowchart TD A["Research Goal: Create domain-adapted LLM for finance"] --> B["Data: Financial Documents & Corpora"] B --> C["Preprocessing: Tokenization & Formatting"] C --> D["Core Methodology: BERT Architecture Adaptation"] D --> E["Training: Domain-specific Fine-tuning"] E --> F["Evaluation: Benchmark Testing"] F --> G["Outcome: FinBERT Model"] F --> H["Outcome: Improved Performance vs. General LLMs"] G --> I["Final Result: State-of-the-art Financial NLP"] H --> I