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Financial information appears in text, tables, charts and speech across languages. A multimodal or multilingual system should be assessed on which signals it actually uses and how well evaluation covers each setting.
The foundation-model and benchmarks guides provide context for evaluating transfer beyond the training language or modality.
XuanYuan 2.0 is a 176-billion parameter Chinese financial chat model built on BLOOM-176B that utilizes a novel hybrid-tuning method to integrate general and financial knowledge while mitigating catastrophic forgetting.
The paper introduces BBT-FinT5, a large-scale Chinese financial pre-trained language model enhanced with knowledge extraction, supported by a 300GB corpus and a comprehensive benchmark, demonstrating superior performance over existing domain-specific models on financial NLP tasks.
DISC-FinLLM is a Chinese financial large language model built on Baichuan-13B using a Multiple Experts Fine-tuning Framework with four specialized LoRA modules, demonstrating superior performance over baselines in financial NLP, computation, and retrieval tasks.
FinEval is a comprehensive Chinese financial benchmark evaluating 19 LLMs across academic knowledge, industry tasks, security, and agent capabilities, revealing that while top models surpass ordinary humans, they still lag behind experts, with general models often outperforming domain-specific ones.
FinTral is a multimodal financial LLM based on Mistral-7B that achieves GPT-4-level performance on nine financial tasks by integrating domain-specific pretraining, RLAIF alignment, and tool-augmented retrieval, while introducing FinSet, a comprehensive benchmark for evaluating financial hallucinations.
The paper proposes PreBit, a multimodal model combining FinBERT-embedded Twitter text with technical analysis data to predict extreme Bitcoin price movements, demonstrating that social media content improves prediction accuracy and enables a lower-risk trading strategy.
Open-FinLLMs introduces a suite of open-source multimodal financial LLMs, including FinLLaMA and FinLLaVA, which outperform general and specialized baselines like GPT-4 and BloombergGPT across text, tabular, time-series, and chart data tasks.
MME-Finance is a bilingual multimodal benchmark evaluating 19 MLLMs on financial charts, revealing that while top models like GPT-4o and Qwen2VL-72B show competence, they struggle significantly with fine-grained visual perception, spatial awareness, and domain-specific chart interpretation.
The paper introduces CFLUE, a comprehensive Chinese financial LLM benchmark with 38K+ knowledge questions and 16K+ application tasks, revealing that while GPT-4 leads in knowledge, lightweight models often match or exceed it in application tasks, and specialized financial LLMs underperform general ones.
The paper introduces FinMME, a large-scale multimodal benchmark with over 11,000 high-quality financial samples across 18 domains, demonstrating that state-of-the-art MLLMs struggle with financial chart reasoning and introducing FinScore to penalize hallucinations.
CFGPT introduces a Chinese financial LLM framework comprising a large-scale pre-training dataset (CFData), a 7B-parameter model (CFLLM) fine-tuned via continued pre-training and supervised instruction tuning, and a deployment system (CFAPP) supporting multimodal inputs and complex financial reasoning tasks.
The paper introduces Plutus-ben, the first Greek financial NLP benchmark, and Plutus-8B, a fine-tuned Greek financial LLM, demonstrating that domain-specific training significantly outperforms general multilingual models in low-resource financial contexts.
The paper introduces Toisón de Oro, a bilingual Spanish-English financial LLM framework including the FinMA-ES model, FIT-ES instruction dataset, and FLARE-ES benchmark, demonstrating that FinMA-ES outperforms GPT-4 in Spanish financial tasks through cross-linguistic transfer.
The paper introduces ICE-PIXIU, an open-source framework comprising the ICE-FIND bilingual instruction dataset, the ICE-INTENT fine-tuned LLM, and the ICE-FLARE benchmark, demonstrating that bilingual fine-tuning significantly enhances Chinese financial NLP performance, often surpassing GPT-4.
FLAME introduces a comprehensive Chinese financial LLM evaluation system comprising certification and scenario benchmarks, revealing that Baichuan4-Finance outperforms other models in professional knowledge and practical application tasks.
This position paper surveys Multimodal Financial Foundation Models (MFFMs), detailing their data-centric approach, agentic applications, and critical challenges in transparency, reasoning, and governance.
Golden Touchstone introduces a comprehensive bilingual benchmark for financial LLMs across eight NLP tasks, revealing that while models excel at sentiment analysis, they struggle with complex reasoning and stock prediction, and proposes Touchstone-GPT as a competitive baseline.