Researchers at FPT Software AI Center Introduce XMainframe: A State-of-the-Art Large Language Model (LLM) Specialized for Mainframe Modernization to Address the $100B Legacy Code Modernization

Researchers at FPT Software AI Center Introduce XMainframe: A State-of-the-Art Large Language Model (LLM) Specialized for Mainframe Modernization to Address the $100B Legacy Code Modernization

Challenges in Using LLMs for Mainframe Modernization:

1. Limited Training on Mainframe Languages:

Existing large language models (LLMs) lack sufficient training on mainframe languages like COBOL, hindering their ability to understand and interact with legacy codebases.

2. Lack of Proper Benchmarks:

The absence of clear benchmarks for evaluating LLMs in the mainframe domain makes it difficult to measure their effectiveness and reliability in modernization tasks.

3. Complexity Beyond Code Generation:

LLMs primarily trained for code generation may not possess the necessary knowledge to effectively modernize mainframe systems, which involve more than just generating COBOL code.

XMainframe

To address these challenges, researchers at FPT Software AI Center have developed XMainframe, a large language model specifically designed for mainframe modernization. This solution includes the creation of an extensive data collection pipeline to produce high-quality training datasets, enhancing XMainframe’s performance in this specialized domain. Additionally, MainframeBench, a comprehensive benchmark, has been introduced to evaluate XMainframe’s knowledge through multiple-choice questions, question answering, and COBOL code summarization. Empirical evaluations show that XMainframe consistently outperforms existing state-of-the-art LLMs in these tasks, underscoring its potential to drive significant advancements in managing and modernizing legacy systems.

Illustration of steps to collect data to build Mainframe:

Details on the data collection process for building Mainframe.

Results on MCQ:

Results of XMainframe’s performance on multiple-choice questions.

Results on Q&A:

Results of XMainframe’s performance on question answering tasks.

Results on Code Summarization:

Results of XMainframe’s performance on COBOL code summarization.

Check out the Paper and GitHub. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.

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Thanks to FPT Software AI Center for the thought leadership/resources for this article. FPT Software AI Center has supported us in this content/article.

The post Researchers at FPT Software AI Center Introduce XMainframe: A State-of-the-Art Large Language Model (LLM) Specialized for Mainframe Modernization to Address the $100B Legacy Code Modernization appeared first on MarkTechPost.

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