Understanding The Effectiveness Of Large Language Models In Code Translation Deepai

Understanding The Effectiveness Of Large Language Models In Code Translation | DeepAI

Understanding The Effectiveness Of Large Language Models In Code Translation | DeepAI To that end, we present a large scale empirical study to investigate the ability of llms, including general llms and code llms, for code translation across pairs of different languages, including c, c , go, java, and python. To that end, we present a large scale empirical study to investigate the ability of llms, including general llms and code llms, for code translation across pairs of different languages, including c, c , go, java, and python.

Talking About Large Language Models | DeepAI

Talking About Large Language Models | DeepAI In this article, we conduct an in depth study of the llms in domain specific code generation. our results demonstrate that llms exhibit sub optimal performance in generating domain specific code, due to their limited proficiency in utilizing domain specific libraries. The goal of our study is to investigate the use of llm tools in the context of translation tasks from code written in query languages to code written in framework specific code languages, exploiting human interaction. Given the promising abilities of large language models (llms) in code synthesis, researchers are exploring their potential to automate code translation. the prerequisite for advancing the state of llm based code translation is to understand their promises and limitations over existing techniques. In this paper, we investigate how well statistical machine translation (smt) models for natural languages could help in migrating source code from one programming language to another.

BigTrans: Augmenting Large Language Models With Multilingual Translation Capability Over 100 ...

BigTrans: Augmenting Large Language Models With Multilingual Translation Capability Over 100 ... Given the promising abilities of large language models (llms) in code synthesis, researchers are exploring their potential to automate code translation. the prerequisite for advancing the state of llm based code translation is to understand their promises and limitations over existing techniques. In this paper, we investigate how well statistical machine translation (smt) models for natural languages could help in migrating source code from one programming language to another. In this paper, we conduct a large scale empirical study to exploit the capabilities and incapabilities of llms in code translation tasks. we first craft a novel benchmark called polyhumaneval by extending humaneval to a multilingual benchmark of 14 languages. Three recent llms of diverse sizes, including gpt 3.5 and llama 13b/7b, are tested with unitrans, and all achieve substantial improvements in terms of computational accuracy and exact match accuracy among almost all translation settings, showing the universal effectiveness of unitrans in practice. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of llms in various software engineering tasks, providing guidance for researchers and developers to optimize. Numerous studies have explored the potential of language models to automate the translation of legacy programming languages into more modern ones, enhancing interoperability and maintainability.

Training Large Language Models Efficiently With Sparsity And Dataflow | DeepAI

Training Large Language Models Efficiently With Sparsity And Dataflow | DeepAI In this paper, we conduct a large scale empirical study to exploit the capabilities and incapabilities of llms in code translation tasks. we first craft a novel benchmark called polyhumaneval by extending humaneval to a multilingual benchmark of 14 languages. Three recent llms of diverse sizes, including gpt 3.5 and llama 13b/7b, are tested with unitrans, and all achieve substantial improvements in terms of computational accuracy and exact match accuracy among almost all translation settings, showing the universal effectiveness of unitrans in practice. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of llms in various software engineering tasks, providing guidance for researchers and developers to optimize. Numerous studies have explored the potential of language models to automate the translation of legacy programming languages into more modern ones, enhancing interoperability and maintainability.

Estimating Large Language Model Capabilities Without Labeled Test Data | DeepAI

Estimating Large Language Model Capabilities Without Labeled Test Data | DeepAI Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of llms in various software engineering tasks, providing guidance for researchers and developers to optimize. Numerous studies have explored the potential of language models to automate the translation of legacy programming languages into more modern ones, enhancing interoperability and maintainability.

Adaptive Machine Translation With Large Language Models | DeepAI

Adaptive Machine Translation With Large Language Models | DeepAI

RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models