Quati: A High-Quality Brazilian Portuguese Information Retrieval Dataset Created by Native Speakers

Core Concepts

This article presents Quati, a high-quality information retrieval dataset specifically designed for the Brazilian Portuguese language. The dataset comprises human-written queries and a curated corpus of documents from high-quality Brazilian Portuguese websites, with relevance annotations provided by a state-of-the-art large language model.

Abstract

The article introduces Quati, a new information retrieval dataset for the Brazilian Portuguese language. The key highlights are:

Motivation: Despite Portuguese being one of the most spoken languages in the world, there is a lack of high-quality information retrieval datasets in that language. Existing datasets are either limited in size and specialized domains or based on translated content, which may not capture the nuances of the target language.

Dataset Creation: The authors created Quati using a semi-automated pipeline to reduce the labeling cost barrier. They used the Portuguese subset of the ClueWeb22 dataset as the document corpus and had native Brazilian Portuguese speakers create 200 test queries. To annotate the relevance of query-passage pairs, the authors leveraged a state-of-the-art large language model (GPT-4), which showed inter-annotator agreement levels comparable to human performance.

Evaluation: The authors assessed the quality of the LLM-based annotations by comparing them with human annotations on a sample of 240 query-passage pairs. The results showed that the LLM annotations correlate with human annotations at a level similar to that of human crowd workers, despite some differences in distinguishing between closer relevance categories.

Retrieval System Evaluation: The authors used the LLM-annotated query-passages to evaluate the effectiveness of various open-source and commercial retrieval systems, establishing a baseline for the Quati dataset.

Availability: Quati is publicly available in two sizes (10M and 1M passages) on the Hugging Face dataset hub, along with the scripts used to generate the dataset.

The authors argue that the semi-automated, cost-effective approach used to create Quati can be replicated to generate high-quality information retrieval datasets for other languages, providing a valuable resource for the development and evaluation of retrieval systems.

Stats

"Despite Portuguese being one of the most spoken languages in the world, there is a scarcity of Information Retrieval (IR) datasets in Portuguese."
"Existing datasets such as REGIS [16] and RCV2 [15]2, though valuable, fall short due to their limited size and specialized domains, such as geoscience and news."
"The total cost for this dataset was U$140.19 (0.03 per query-passage) for an average of 97.78 annotated passages per query."

Quotes

"To address those issues we created Quati, a Brazilian Portuguese evaluation dataset, comprising human-written queries and a high-quality native corpus."
"We use a Large Language Model (LLM) to judge a passage's relevance for a given query, publishing a cost-effective pipeline to create an IR evaluation dataset with an arbitrary number of annotated passages per query."
"The usage of a modular semi-automated pipeline, allows the dataset construction method to be replicated to create high-quality IR datasets for other languages."

Key Insights Distilled From

Quati

by Mirelle Buen... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06976.pdf

Deeper Inquiries

How can the Quati dataset be used to improve the performance of information retrieval systems for the Brazilian Portuguese language?

The Quati dataset can significantly enhance the performance of information retrieval systems for Brazilian Portuguese by providing a high-quality evaluation dataset specifically tailored to the language. Native speakers formulated the queries in the dataset, ensuring that they capture the information needs and social-cultural aspects of the Brazilian community accurately. The curated set of documents from high-quality Brazilian Portuguese websites further enhances the relevance and authenticity of the dataset.
Information retrieval systems can utilize the Quati dataset to train and evaluate their algorithms, enabling them to better understand and respond to the information needs of users in Brazilian Portuguese. By using state-of-the-art Large Language Models (LLMs) to label the query-document pairs, the dataset ensures a high level of accuracy and relevance in the annotations. This allows for the development and refinement of retrieval systems that can effectively cater to the specific requirements of Brazilian Portuguese speakers.
The diversity of open-source and commercial retrievers evaluated using the Quati dataset serves as a baseline for system performance, enabling researchers and developers to compare and improve their retrieval algorithms. By leveraging the diverse range of retrieval systems and the annotated query-document pairs in the dataset, developers can fine-tune their models, optimize retrieval strategies, and ultimately enhance the overall performance of information retrieval systems for Brazilian Portuguese.

What are the potential limitations or biases in the Quati dataset, and how can they be addressed in future iterations?

One potential limitation of the Quati dataset could be the reliance on LLMs for annotating query-document pairs, which may introduce biases or inaccuracies in the relevance judgments. While the LLM annotations showed a correlation with human annotators, there is still room for improvement in ensuring the accuracy and consistency of the annotations.
To address this limitation in future iterations, it is essential to continue refining the annotation methodology and prompt engineering for the LLMs. By providing more detailed and specific prompts, as well as incorporating feedback mechanisms to validate the annotations, the dataset can achieve higher levels of accuracy and reduce potential biases in the relevance judgments.
Another potential limitation could be the representativeness of the document sources in the dataset. Ensuring a diverse and comprehensive selection of websites and domains can help mitigate biases and improve the generalizability of the dataset for a wide range of information retrieval tasks in Brazilian Portuguese.
Additionally, ongoing efforts to expand the dataset with more queries, documents, and annotations can help address limitations related to dataset size and coverage. Continuous evaluation and validation of the dataset with human annotators can also help identify and correct any biases or inconsistencies in the annotations.

How can the semi-automated approach used to create Quati be adapted to generate high-quality information retrieval datasets for other less-resourced languages?

The semi-automated approach used to create the Quati dataset can be adapted to generate high-quality information retrieval datasets for other less-resourced languages by following a similar methodology with necessary adjustments for language-specific nuances and characteristics. Here are some steps to adapt the approach:

Data Collection and Preparation: Identify a large corpus in the target language and extract passages for the dataset. Ensure the corpus is diverse and representative of the language's content.

Query Creation: Develop a set of queries that reflect the information needs of native speakers in the target language. Consider different themes, scopes, and types of questions to cover a wide range of information needs.

Passage Retrieval: Utilize a mix of retrieval systems to retrieve passages for annotation. Include both strong baseline systems and weaker systems to ensure diversity in the dataset.

Annotation: Use LLMs or other automated methods to annotate the query-document pairs for relevance. Validate the annotations with human annotators to ensure accuracy and consistency.

Evaluation and Iteration: Evaluate the dataset with retrieval systems and human annotators to assess its quality and effectiveness. Incorporate feedback to improve the dataset in future iterations.

By following these steps and adapting the approach to the specific characteristics of the target language, researchers can create high-quality information retrieval datasets for less-resourced languages, similar to the Quati dataset for Brazilian Portuguese. This approach can help bridge the gap in resources and support the development of effective retrieval systems for a wide range of languages.