Latxa: An Open Large Language Model and Comprehensive Evaluation Suite for the Basque Language

Concetti Chiave

Latxa is a family of large language models for Basque ranging from 7 to 70 billion parameters, which substantially outperforms previous open models and is competitive with GPT-4 Turbo on language proficiency and understanding tasks.

Sintesi

The paper introduces Latxa, a family of large language models for the Basque language ranging from 7 to 70 billion parameters. Latxa is based on the Llama 2 model, which the authors further pretrained on a new Basque corpus comprising 4.3 million documents and 4.2 billion tokens.
To address the scarcity of high-quality benchmarks for Basque, the authors also introduce 4 new multiple-choice evaluation datasets: EusProficiency (5,169 questions from official language proficiency exams), EusReading (352 reading comprehension questions), EusTrivia (1,715 trivia questions), and EusExams (16,774 questions from public examinations).
The authors extensively evaluate the Latxa models and show that they outperform all previous open models by a large margin. The 70 billion parameter Latxa model is also competitive with GPT-4 Turbo in language proficiency and understanding tasks, despite lagging behind in reading comprehension and knowledge-intensive tasks.
Both the Latxa family of models and the new pretraining corpora and evaluation datasets are publicly available under open licenses, enabling reproducible research on building large language models for low-resource languages.

Statistiche

The Latxa training corpus comprises 4.3 million documents and 4.2 billion tokens.
The 7B, 13B, and 70B Latxa models were trained for 10,000 steps with a sequence length of 4,096 tokens and an effective batch size of 1 million tokens.
The training of the 7B, 13B, and 70B Latxa models consumed 952.53, 2,518.0, and 30,266.0 GPU hours, respectively, resulting in a total carbon footprint of 4,408.7 kg CO2 equivalent.

Citazioni

"Latxa is a family of large language models for Basque ranging from 7 to 70 billion parameters, which substantially outperforms previous open models and is competitive with GPT-4 Turbo on language proficiency and understanding tasks."
"Both the Latxa family of models, as well as our new pretraining corpora and evaluation datasets, are publicly available under open licenses."

Approfondimenti chiave tratti da

Latxa

by Julen Etxani... alle arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20266.pdf

Domande più approfondite

How can the continued pretraining approach used for Latxa be applied to other low-resource languages to develop high-performing language models?

The continued pretraining approach used for Latxa can be applied to other low-resource languages by following a systematic process. Here are the steps to apply this approach:

Data Collection: Gather a diverse and high-quality dataset in the target language. This dataset should cover various domains and be representative of the language's usage.

Preprocessing: Clean and preprocess the data to remove noise, duplicates, and irrelevant information. Ensure that the dataset is well-structured and ready for training.

Model Selection: Choose a base model that has shown success in other languages and is suitable for continued pretraining. Models like Llama 2 or similar large language models can serve as a good starting point.

Continued Pretraining: Continue pretraining the selected model on the new dataset in the target language. This process helps the model adapt to the linguistic nuances and patterns specific to that language.

Fine-Tuning: Fine-tune the pre-trained model on specific tasks or datasets relevant to the target language. This step helps the model specialize in certain areas and improve performance on specific tasks.

Evaluation: Evaluate the performance of the model on a diverse set of benchmarks and tasks to assess its capabilities in the target language. This step helps in identifying strengths and areas for improvement.

By following these steps, the continued pretraining approach can be effectively applied to other low-resource languages, leading to the development of high-performing language models tailored to specific linguistic contexts.

What are the potential biases and limitations of the Latxa models, and how can they be further mitigated?

The Latxa models, like any language model, may have biases and limitations that can impact their performance and reliability. Some potential biases and limitations of the Latxa models include:

Data Bias: The training data used for pretraining may contain biases present in the original sources, leading to biased predictions and outputs.

Linguistic Biases: The model may exhibit biases based on the linguistic patterns present in the training data, which can affect its understanding and generation of text.

Cultural Biases: The model's outputs may reflect cultural biases present in the training data, potentially leading to inaccurate or inappropriate responses.

Domain Specificity: The model's performance may vary across different domains or topics based on the distribution of data in the training set.

To mitigate these biases and limitations, the following strategies can be employed:

Diverse Training Data: Incorporate diverse and representative datasets to reduce biases and ensure a more balanced training corpus.

Bias Detection: Implement bias detection mechanisms to identify and mitigate biases in the model's outputs.

Fairness Evaluation: Conduct fairness evaluations to assess the model's performance across different demographic groups and ensure equitable outcomes.

Regular Updates: Continuously update and retrain the model with new data to adapt to evolving language patterns and mitigate biases that may arise over time.

By implementing these strategies, the biases and limitations of the Latxa models can be addressed, leading to more robust and reliable language models.

What other types of tasks or benchmarks could be developed to more comprehensively evaluate the capabilities of large language models for the Basque language and other low-resource languages?

To comprehensively evaluate the capabilities of large language models for the Basque language and other low-resource languages, the following types of tasks or benchmarks could be developed:

Fact-Checking: Create a fact-checking dataset in the target language to assess the model's ability to verify the accuracy of statements and information.

Sentiment Analysis: Develop a sentiment analysis dataset to evaluate the model's understanding of emotions and opinions expressed in text.

Named Entity Recognition: Build a named entity recognition dataset to test the model's ability to identify and classify named entities in the target language.

Text Summarization: Create a text summarization benchmark to evaluate the model's capability to generate concise and informative summaries of text passages.

Dialogue Systems: Develop a dialogue dataset to assess the model's performance in engaging and contextually relevant conversations in the target language.

Multimodal Tasks: Introduce tasks that combine text with other modalities like images or audio to evaluate the model's multimodal understanding and generation capabilities.

By incorporating these diverse tasks and benchmarks, the evaluation of large language models for low-resource languages can be more comprehensive, covering a wide range of linguistic and cognitive abilities.