The List / Categories
Financial language models range from domain-specific encoders to instruction-tuned and general-purpose generative models. This guide groups work on model adaptation without treating a finance label as evidence of superior performance.
Pair model papers with the benchmarks and multilingual guides to examine whether improvements transfer.
BloombergGPT is a 50 billion parameter language model trained on a mixed corpus of 363 billion financial tokens and 345 billion general tokens, demonstrating superior performance on financial benchmarks while maintaining competitive general NLP capabilities.
FinBERT, a BERT-based language model further pre-trained on financial text, achieves state-of-the-art performance in financial sentiment analysis on the Financial PhraseBank and FiQA datasets, significantly outperforming traditional machine learning and other transfer learning baselines like ULMFit and ELMo.
Discovered through citation/reference expansion; connected to 4 source papers.
FinGPT introduces an open-source, data-centric framework for financial LLMs that utilizes lightweight LoRA fine-tuning and market-driven reinforcement learning to achieve superior financial sentiment analysis compared to proprietary and baseline models.
FinBERT is a domain-specific BERT model pretrained on 4.9 billion tokens of financial text that significantly outperforms generic BERT on financial sentiment classification tasks.
FinBERT is a domain-specific BERT model pre-trained on large-scale financial corpora using six multi-task self-supervised objectives, achieving state-of-the-art performance on financial sentence boundary detection, sentiment analysis, and question answering benchmarks.
PIXIU introduces FinMA, an open-source financial LLM fine-tuned on the FIT dataset, and FLARE, a benchmark evaluating financial NLP and stock prediction, showing FinMA outperforms general LLMs on NLP tasks but struggles with quantitative reasoning.
The paper introduces FLANG, a financial language model using preferential masking of financial terms and phrases, and FLUE, a comprehensive benchmark suite, demonstrating that domain-specific pre-training significantly outperforms general and prior financial models on sentiment analysis, news classification, and question answering tasks.
This study demonstrates that zero-shot prompting of ChatGPT 3.5 significantly outperforms FinBERT in classifying sentiment from forex news headlines and correlates more strongly with market returns, highlighting the value of prompt engineering in financial NLP.
Instruct-FinGPT improves financial sentiment analysis by instruction-tuning LLaMA-7B, significantly outperforming FinBERT and ChatGPT in numerical sensitivity and contextual understanding.
FinGPT introduces a cost-effective instruction tuning benchmark for open-source LLMs, demonstrating that task-specific and multi-task tuning significantly improves financial NLP performance, while zero-shot generalization remains challenging for most models except ChatGLM2.
FinGPT introduces an open-source framework for democratizing financial LLMs by automating the collection of real-time data from 34+ sources and enabling lightweight fine-tuning via LoRA and a novel Reinforcement Learning with Stock Prices (RLSP) method.
The paper introduces FinBERT-FOMC, a fine-tuned FinBERT model enhanced with a Sentiment Focus preprocessing method, which significantly improves sentiment classification accuracy on complex FOMC Minutes sentences containing contrasting conjunctions.
The paper demonstrates that instruction fine-tuning smaller open-source LLMs like Mistral-7B and Llama3-8B significantly improves financial text classification, with model merging via MergeKit effectively mitigating catastrophic forgetting on unseen tasks.
The paper evaluates LLM reasoning in financial sentiment analysis by introducing a Financial Attribute Prompting framework, revealing that models like GPT-3.5 and PaLM-2 lack inherent capabilities in numerical and comparative reasoning despite improvements from explicit prompts.
The paper introduces an open, continuously updated FinLLM leaderboard developed with the Linux Foundation and Hugging Face to standardize the evaluation of financial language models and agents across multimodal tasks, addressing the static nature of existing benchmarks.
This study demonstrates that GPT-4o, when optimized through systematic prompt engineering, outperforms the domain-specific FinBERT model in financial sentiment analysis across business, health, and technology sectors by up to 10%.
The paper demonstrates that multi-task fine-tuning on a cocktail of financial and general datasets significantly enhances LLM performance on specific downstream tasks, enabling small models like Phi-3-Mini to surpass larger models like GPT-4-o, though this does not necessarily improve broader domain knowledge.
The paper demonstrates that zero-shot financial sentiment classification aligns better with human judgment when using intuitive, direct prompting (System 1) rather than deliberative chain-of-thought reasoning (System 2), which often leads to overthinking and reduced accuracy.
The paper introduces FinCDM, a cognitive diagnosis framework for evaluating financial LLMs at the knowledge-skill level, revealing hidden gaps and specialization patterns that aggregate benchmarks miss.
FLaME introduces a holistic benchmarking suite for financial NLP, evaluating 23 foundation and reasoning-reinforced LMs across 20 datasets to reveal that no single model dominates all tasks, with significant performance gaps in numeric reasoning and causal analysis, while highlighting cost-performance trade-offs for open-weight models.