Construction of a Japanese Financial Benchmark for Large Language Models

Belangrijkste concepten

GPT-4 excels in the Japanese financial benchmark, showcasing effective differentiation among models.

Samenvatting

The content discusses the construction of a benchmark specific to the Japanese and financial domains for large language models. It highlights the necessity of domain-specific benchmarks to evaluate LLMs effectively. The study focuses on various tasks related to the Japanese financial domain, including sentiment analysis, fundamental knowledge questions in securities analysis, auditing tasks, multiple-choice questions for financial planner exams, and practice exams for securities broker representative tests. The performance of different models is evaluated using these benchmarks, with GPT-4 demonstrating outstanding results. The study emphasizes the importance of accurate evaluation tasks for developing high-performance LLMs.
Structure:

Introduction to Large Language Models (LLMs)

Overview of recent advancements in LLMs.

Evaluation Tasks for LLMs

Discussion on existing evaluation tasks and their limitations.

Focus on Japanese Financial Domain

Importance of evaluating LLMs in the Japanese financial domain.

Description of Benchmark Tasks

Detailed explanation of each benchmark task.

Experiments and Results

Methodology used to measure benchmarks and summary of results.

Discussion on Model Performance

Analysis of model performance with a focus on GPT-4.

Conclusion and Future Studies

Summary of findings and suggestions for future research.

Statistieken

According to our analysis, our benchmark can differentiate benchmark scores among models in all performance ranges by combining tasks with different difficulties.
In this dataset, 4334 positive, 3131 negative, and 258 neutral responses were observed.

Citaten

"We constructed a new LLM benchmark specialized for Japanese financial tasks."
"The GPT-4 series exhibited overwhelming performance."

Belangrijkste Inzichten Gedestilleerd Uit

Construction of a Japanese Financial Benchmark for Large Language Models

by Masanori Hir... om arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15062.pdf

Diepere vragen

How can domain-specific benchmarks enhance the evaluation process for large language models?

Domain-specific benchmarks play a crucial role in evaluating the performance of large language models (LLMs) by providing targeted tasks that reflect real-world applications within a particular field. These benchmarks help assess how well LLMs can handle specialized content and challenges unique to that domain. By focusing on specific areas such as finance in this context, domain-specific benchmarks enable a more accurate assessment of an LLM's capabilities within that industry. They allow researchers and developers to tailor evaluations to relevant tasks, ensuring that the model's performance aligns with practical requirements.
Furthermore, domain-specific benchmarks provide insights into how effectively an LLM can understand and generate content related to complex topics like financial analysis or auditing. This focused evaluation helps identify strengths and weaknesses specific to the domain, guiding improvements in model training and fine-tuning for better performance in real-world scenarios.

What are potential drawbacks or limitations when relying solely on large language models like GPT-4?

While large language models like GPT-4 exhibit impressive performance across various tasks, there are several drawbacks and limitations associated with relying solely on them:

Generalization Limitations: Despite their high accuracy on diverse tasks, LLMs may struggle with nuanced understanding or context-dependent information due to their pre-trained nature.

Bias Amplification: Large language models have been known to amplify biases present in their training data, leading to potentially biased outputs or decisions.

Lack of Domain Expertise: Without specific training on niche domains like finance, relying solely on general-purpose LLMs may result in suboptimal performance for specialized tasks requiring deep industry knowledge.

Costly Training & Inference: Implementing and utilizing large-scale LLMs like GPT-4 can be computationally expensive both during training phases and inference processes.

Ethical Concerns: The ethical implications surrounding privacy violations, misinformation propagation, or unintended consequences from unrestricted use of powerful AI systems need careful consideration when relying heavily on these models.

How might incorporating financial documents into training impact the performance of language models?

Incorporating financial documents into the training data of language models can significantly impact their performance by enhancing their understanding of specialized terminology, contexts, and nuances prevalent in the finance domain:

Improved Contextual Understanding: Exposure to financial texts allows language models to learn intricate details about market trends, investment strategies, regulatory frameworks which enhances their contextual comprehension.

Specialized Vocabulary Acquisition: Financial documents introduce unique vocabulary specific to banking institutions,
economic theories which enriches the model's lexicon enabling more accurate generation of text related
specifically towards finance-related queries

3 .Enhanced Task Performance: Language Models trained using financial documents tend
perform better at finance-focused benchmark tests as they develop expertise through exposure
extensive range materials
By integrating financial texts during pre-training stages,
language become adept at handling complex scenarios typical
in this sector resulting improved overall task performances