Suvach: A Novel Hindi Question Answering Benchmark Generated Using Large Language Models

Core Concepts

Suvach is a novel benchmark for evaluating Hindi question answering models, generated using large language models to overcome the limitations of machine-translated datasets.

Abstract

The paper introduces Suvach, a new benchmark for evaluating extractive question answering (QA) in Hindi. The key points are: Current Indic language QA benchmarks often rely on machine translation of English datasets, which can introduce biases and inaccuracies. To address this, the authors propose a methodology to generate a high-quality Hindi QA dataset using large language models (LLMs). This involves: Creating prompts with relevant context from Hindi Wikipedia dumps Using LLMs to generate question-answer pairs from the prompts Validating the generated content for relevance, accuracy, and clarity The resulting Suvach dataset contains over 100,000 question-answer pairs, with an average of 1,200 tokens per question. It provides three levels of difficulty: Question only Question with context Question with context and multiple-choice options The authors argue that this LLM-powered approach to benchmark generation can be generalized to create high-quality datasets for other Indic languages, fostering advancements in Indic NLP research.

Stats

"Recent breakthroughs in Large Language Models (LLMs), particularly those following the advent of ChatGPT, were transformative." "This dataset consists of over 100k question answers in Hindi, with 1200 tokens per question on average."

Quotes

"While machine translation offers a temporary solution, it is not a sustainable approach for developing long-term, large-scale benchmarks across all Indian languages." "This finding suggests a promising avenue for LLM-powered benchmark creation for low-resource languages."

Key Insights Distilled From

Suvach -- Generated Hindi QA benchmark

by Vaishak Nara... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19254.pdf

Deeper Inquiries

How can the Suvach benchmark be extended to cover a broader range of question types beyond extractive QA, such as open-ended questions or multi-hop reasoning?

To extend the Suvach benchmark to cover a broader range of question types, such as open-ended questions or multi-hop reasoning, the methodology can be adapted to incorporate more complex question structures. For open-ended questions, the prompts can be modified to encourage free-form responses rather than selecting from predefined choices. This would require the generation of prompts that allow for varied and diverse answers, challenging the models to generate responses that are not limited to a set of options. For multi-hop reasoning, the prompts can be designed to include multiple pieces of information that require the model to connect and reason across different contexts. This would involve creating prompts that involve multiple steps or pieces of information, testing the model's ability to perform complex reasoning tasks. By expanding the prompts and validation criteria to accommodate these different question types, the Suvach benchmark can evolve to assess a wider range of capabilities in Hindi language models beyond extractive QA.

What are the potential biases or limitations in the Hindi Wikipedia data used to generate the Suvach dataset, and how can they be mitigated?

One potential bias in using Hindi Wikipedia data for generating the Suvach dataset is the inherent bias present in the Wikipedia articles themselves. Wikipedia articles may reflect certain perspectives or biases of the contributors, leading to skewed information or limited coverage of certain topics. This bias can impact the quality and diversity of the dataset generated from these articles. To mitigate this bias, it is essential to diversify the sources of data used for generating prompts. Incorporating data from multiple sources, such as reputable news websites, academic papers, or government reports, can help reduce the reliance on a single biased source like Wikipedia. Additionally, implementing a robust validation process that checks for accuracy, relevance, and diversity of information in the generated questions can help identify and address any biases present in the dataset. Furthermore, conducting a thorough analysis of the dataset to identify and mitigate any biases or limitations before using it for training and evaluation can help ensure the quality and fairness of the Suvach benchmark.

How can the Suvach benchmark be used to drive the development of more robust and generalizable Hindi language models, beyond just question answering capabilities?

The Suvach benchmark can serve as a valuable resource for driving the development of more robust and generalizable Hindi language models beyond question answering capabilities by providing a standardized evaluation framework for assessing various NLP tasks. By expanding the benchmark to include different types of tasks such as text generation, summarization, sentiment analysis, and more, researchers and developers can evaluate the performance of their models across a wide range of linguistic tasks. Additionally, the Suvach benchmark can be used to fine-tune existing language models or train new models specifically tailored to the nuances of the Hindi language. By leveraging the high-quality dataset generated by Suvach, researchers can improve the performance of their models on Hindi-specific tasks and enhance the overall quality of NLP applications in Hindi. Furthermore, the insights gained from working with the Suvach benchmark can inform the development of new techniques and approaches for handling low-resource languages, paving the way for advancements in Hindi NLP research and contributing to the growth of the field as a whole.