FIN-bench-v2: A Unified and Robust Benchmark Suite for Evaluating Finnish Large Language Models
FIN-bench-v2 evaluates Finnish LLMs using a unified benchmark suite, validated with a 2.15B parameter model.
Key Findings
Methodology
FIN-bench-v2 employs a unified benchmark suite covering tasks like reading comprehension and sentiment analysis. All datasets are converted to HuggingFace format and evaluated using a 2.15B parameter decoder model to assess task robustness and consistency.
Key Results
- In the ARC Challenge task, models performed well under both CF and MCF prompts, showing high monotonicity and signal-to-noise ratio.
- For the GoldenSwag dataset, performance under MCF prompts was near random, highlighting task challenges.
- In the TruthfulQA task, models exhibited a high Non-Random Performance Coefficient, indicating potential in generative tasks.
Significance
FIN-bench-v2 provides a comprehensive framework for evaluating models in low-resource languages, addressing long-standing issues of data quality and task design. By introducing diverse tasks and prompt forms, it enhances model generalization across tasks.
Technical Contribution
The study introduces new evaluation metrics like Monotonicity Index and Signal-to-Noise Ratio, offering deep insights into task stability and model learning trends. These metrics help identify tasks with stable performance during training.
Novelty
FIN-bench-v2 is the first to integrate multiple Finnish benchmarks into a unified framework, with human annotations enhancing the quality of translated datasets.
Limitations
- Some tasks under MCF prompts performed poorly, possibly due to translation dataset quality issues.
- Model performance in some generative tasks still needs improvement.
Future Work
Future research directions include expanding FIN-bench-v2 to cover more domains like medicine and geography, and optimizing the quality of translation datasets.
AI Executive Summary
FIN-bench-v2 is a unified benchmark suite for evaluating Finnish large language models. Existing evaluation resources are predominantly in English, limiting the development of models for low-resource languages. FIN-bench-v2 addresses this by integrating various benchmarks covering tasks like reading comprehension and sentiment analysis.
The suite converts all datasets to HuggingFace format and uses a 2.15B parameter decoder model to evaluate task robustness and consistency. By introducing metrics like Monotonicity Index and Signal-to-Noise Ratio, FIN-bench-v2 identifies tasks with stable performance during training.
Experimental results show strong performance in the ARC Challenge task but poor performance under MCF prompts for the GoldenSwag dataset. This indicates the significance of FIN-bench-v2 in enhancing model generalization but highlights the need for further optimization of translation dataset quality.
Deep Analysis
Background
Large language models (LLMs) have made significant strides in natural language processing, particularly in English. However, the lack of evaluation resources for low-resource languages like Finnish limits model development. FIN-bench-v2 provides a comprehensive evaluation framework by integrating various Finnish benchmarks.
Core Problem
Existing evaluation resources are predominantly in English, limiting the development of models for low-resource languages. Data quality and task design deficiencies are major bottlenecks, leading to unstable model performance in these tasks.
Innovation
FIN-bench-v2's core innovation lies in its unified evaluation framework and diverse task design. By introducing metrics like Monotonicity Index and Signal-to-Noise Ratio, the suite identifies tasks with stable performance during training.
Methodology
- �� Convert all datasets to HuggingFace format for long-term maintainability.
- �� Use a 2.15B parameter decoder model for pre-training to assess task robustness.
- �� Introduce metrics like Monotonicity Index and Signal-to-Noise Ratio for task stability analysis.
Experiments
The experimental design includes multiple benchmark datasets like ARC Challenge, GoldenSwag, and TruthfulQA. Models are pre-trained with 2.15B parameters to evaluate performance across different tasks and prompt forms.
Results
In the ARC Challenge task, models performed well under both CF and MCF prompts, showing high monotonicity and signal-to-noise ratio. For the GoldenSwag dataset, performance under MCF prompts was near random, highlighting task challenges.
Applications
FIN-bench-v2 can be used to evaluate and improve the performance of low-resource language models, particularly in tasks like reading comprehension and commonsense reasoning. Its diverse task design enhances model generalization.
Limitations & Outlook
Some tasks under MCF prompts performed poorly, possibly due to translation dataset quality issues. Additionally, model performance in some generative tasks still needs improvement. Future research should focus on optimizing translation dataset quality.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking a meal. FIN-bench-v2 is like a new recipe book that not only tells you how to cook but also offers different methods and spice options. Each step is detailed to ensure you make a delicious dish. This recipe book is especially useful for those wanting to try new dishes because it offers various choices and alternatives. Just like experimenting with different spices in the kitchen, FIN-bench-v2 gives you more options and flexibility in model evaluation.
ELI14 Explained like you're 14
Imagine you're playing a new video game with many levels, each with different challenges. FIN-bench-v2 is like a game guide that tells you the best way to beat each level. You can choose different weapons and skill combos to defeat enemies. This guide is perfect for players looking to improve their game skills because it offers detailed strategies and tips. Just like trying different strategies in a game, FIN-bench-v2 gives you more options and flexibility in model evaluation.
Glossary
Benchmark
A set of standardized tasks used to evaluate model performance.
FIN-bench-v2 integrates various benchmarks to evaluate Finnish LLMs.
Monotonicity Index
A metric that measures whether a model's performance consistently improves during training.
Used to assess task stability and model learning trends.
Signal-to-Noise Ratio
A metric that measures the ratio of signal to noise in a task during training.
Used to identify tasks with stable performance.
Multiple-choice Formulation
A task prompt form requiring the model to select the correct answer from multiple options.
Used in FIN-bench-v2 to assess model selection ability.
Generative Task
A task requiring the model to generate free-form text output.
Used in FIN-bench-v2 to evaluate model generative capabilities.
Open Questions Unanswered questions from this research
- 1 How to improve the quality of translation datasets to enhance model performance under MCF prompts?
- 2 How to improve model accuracy and consistency in generative tasks?
Applications
Immediate Applications
Model Evaluation
Researchers can use FIN-bench-v2 to evaluate and improve the performance of Finnish LLMs.
Long-term Vision
Multilingual Support
FIN-bench-v2's framework can be extended to other low-resource languages, enhancing global language model performance.
Abstract
We introduce FIN-bench-v2, a unified benchmark suite for evaluating large language models in Finnish. FIN-bench-v2 consolidates Finnish versions of widely used benchmarks together with an updated and expanded version of the original FIN-bench into a single, consistently formatted collection, covering multiple-choice and generative tasks across reading comprehension, commonsense reasoning, sentiment analysis, world knowledge, and alignment. All datasets are converted to HuggingFace Datasets, which include both cloze and multiple-choice prompt formulations with five variants per task, and we incorporate human annotation or review for machine-translated resources such as GoldenSwag and XED. To select robust tasks, we pretrain a set of 2.15B-parameter decoder-only models and use their learning curves to compute monotonicity, signal-to-noise, non-random performance, and model ordering consistency, retaining only tasks that satisfy all criteria. We further evaluate a set of larger instruction-tuned models to characterize performance across tasks and prompt formulations. All datasets, prompts, and evaluation configurations are publicly available via our fork of the Language Model Evaluation Harness at https://github.com/LumiOpen/lm-evaluation-harness. Supplementary resources are released in a separate repository at https://github.com/TurkuNLP/FIN-bench-v2.