{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/empirical-studies-of-parameter-efficient","title":"Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R","arxiv_id":"2405.01553","date":"2024-03-16","proceeding":null,"authors":["Amirreza Esmaeili","Iman Saberi","Fatemeh H. Fard"],"abstract":"Parameter Efficient Fine-Tuning (PEFT) methods are proposed as an alternative fine-tuning approach for Large Language Models (LLM) to minimize high training costs. While prior research demonstrates the effectiveness of PEFT methods in knowledge transfer using smaller language models, their application to larger LLMs, particularly in low-resource and unseen programming languages such as R, remains under-explored. In this work, we evaluate PEFT methods, LoRA, Compacter, and IA^3 on LLMs for code summarization and generation, with a particular emphasis on knowledge transfer to R as an unseen under-explored target language. Our experiments reveal that LoRA consistently outperforms Compacter and IA^3 in all settings, while Compacter offers significant resource efficiency with minimal performance trade-offs. Additionally, we find that the number of trainable parameters has a greater influence on the functional accuracy of the generated code than PEFT architecture. Our study can direct future research in developing code intelligent tasks for unseen languages including R, as well as the choice of PEFT methods for knowledge transfer, especially when balancing the computational cost and performance.","url_abs":"https://arxiv.org/abs/2405.01553v2","url_pdf":"https://arxiv.org/pdf/2405.01553v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"empirical-studies-of-parameter-efficient","repo_url":"https://github.com/Amirresm/empirical_peft_suite","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"code-summarization-1","task_name":"Code Summarization"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"parameter-efficient-fine-tuning","task_name":"parameter-efficient fine-tuning"}],"methods":[{"method_slug":"adafactor","method_name":"Adafactor"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"codet5","method_name":"CodeT5"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"glu","method_name":"Gated Linear Unit"},{"method_slug":"inverse-square-root-schedule","method_name":"Inverse Square Root Schedule"},{"method_slug":"llama","method_name":"LLaMA"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sentencepiece","method_name":"SentencePiece"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"t5","method_name":"T5"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}