Recent research highlights the success of large language models (LLMs) trained on code, which excel in various software engineering tasks. These models are divided into three main paradigms: (i) Code LLMs specialized in completing code, (ii) Code LLMs for specific tasks fine-tuned for individual tasks, and (iii) Code LLMs fine-tuned to instructions, experts in adhering to instructions. human and demonstrate robustness in handling new tasks. Recent instruction-tuned code LLMs, such as WizardCoder and OctoCoder, have notably achieved state-of-the-art performance on various tasks without requiring task-specific tuning.
To delve deeper into the opportunities identified, researchers from Monash University and ServiceNow Research present ASTRAIOS, a collection comprising 28 instructionally tailored Code LLMs. These models undergo fine tuning using seven tuning methods based on StarCoder's base models, specifically, models of size 1B, 3B, 7B, and 16 B. They perform instructional tuning on these models using the data set. OctoPack's CommitPackFT to ensure a balance. improvement of their subsequent capabilities.
They employ PEFT configurations aligned with Hugging Face best practices and integrate selected PEFT methods from recent frameworks. They initially examine the scalability of different tuning methods by evaluating the cross-entropy loss during instruction tuning. This evaluation focuses specifically on evaluating model size and training time scales.
Its main evaluation revolves around five representative code-related tasks: clone detection, defect detection, code synthesis, code repair, and code explanation. Additionally, they perform more detailed analyzes of the tuning methods, examining model robustness and code security. This evaluation involves evaluating the models' ability to generate Code based on perturbed examples and determining potential vulnerabilities in the generated Code.
Larger PEFT code LLMs excel in code generation tasks, but do not demonstrate similar advantages in code understanding tasks such as clone detection and defect detection. As model size increases, task performance in generation improves, but raises concerns regarding susceptibility to adversarial examples and a bias toward unsafe code.
Their study delves into the relationship between updated parameters, cross-entropy loss, and task performance. They determine that the final loss of smaller PEFT models can be used to predict that of larger ones. Furthermore, there is a strong correlation between the last loss and overall performance on subsequent tasks.
The correlation between model loss and updated parameters is inconsistent across different model sizes in our analysis. However, a noteworthy finding is the consistency in relative loss performance across various model sizes when comparing other fitting methods. This consistency implies that the improvements achieved by each fitting method are comparable, regardless of the scale of the model. Consequently, the loss observed in smaller models fitted using different methods can serve as a valuable indicator for predicting the performance of larger models.
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Arshad is an intern at MarktechPost. He is currently pursuing his international career. Master's degree in Physics from the Indian Institute of technology Kharagpur. Understanding things down to the fundamental level leads to new discoveries that lead to the advancement of technology. He is passionate about understanding nature fundamentally with the help of tools such as mathematical models, machine learning models, and artificial intelligence.
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