Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R

• URL: http://arxiv.org/abs/2405.01553v1
• Date: Sat, 16 Mar 2024 03:12:45 GMT
• Title: Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R
• Authors: Amirreza Esmaeili, Iman Saberi, Fatemeh H. Fard,
• Abstract summary: Large Langauge Models (LLMs) have gained a lot of attention in the Software Engineering (SE) community. In this work, we empirically study PEFT methods, LoRA and Compacter, on CodeT5 and CodeLlama. We will assess their performance compared to fully fine-tuned models, whether they can be used for knowledge transfer from natural language models to code, and their ability to adapt the learned knowledge to an unseen language.
• Score: 1.9799527196428242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, Large Langauge Models (LLMs) have gained a lot of attention in the Software Engineering (SE) community. LLMs or their variants pre-trained on code are used for many SE tasks. A main approach for adapting LLMs to the downstream task is to fine-tune the models. However, with having billions-parameters-LLMs, fine-tuning the models is not practical. An alternative approach is using Parameter Efficient Fine Tuning (PEFT), in which the model parameters are frozen and only a few added parameters are trained. Though the LLMs are used for programming languages such as Python and Java widely, their capability for low-resource languages is limited. In this work, we empirically study PEFT methods, LoRA and Compacter, on CodeT5 and CodeLlama. We will assess their performance compared to fully fine-tuned models, whether they can be used for knowledge transfer from natural language models to code (using T5 and Llama models), and their ability to adapt the learned knowledge to an unseen language. For the unseen language, we aim to study R, as it has a wide community. The adaptability with less computational costs makes LLMs accessible in scenarios where heavy computational resources are not available. Moreover, studying R opens new opportunities for using LLMs for other languages. We anticipate our findings to showcase the capabilities of PEFT for code LLMs for R and reveal the improvement areas.

Related papers

• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Enhancing Discriminative Tasks by Guiding the Pre-trained Language Model with Large Language Model's Experience [4.814313782484443]
  Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks. We use LLMs to generate domain-specific data, thereby improving the performance of pre-trained LMs on the target tasks.
  arXiv  Detail & Related papers  (2024-08-16T06:37:59Z)
• Verbalized Machine Learning: Revisiting Machine Learning with Language Models [63.10391314749408]
  We introduce the framework of verbalized machine learning (VML) VML constrains the parameter space to be human-interpretable natural language. We empirically verify the effectiveness of VML, and hope that VML can serve as a stepping stone to stronger interpretability.
  arXiv  Detail & Related papers  (2024-06-06T17:59:56Z)
• Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning [35.03338699349037]
  We propose a novel in-context learning framework, FeatLLM, which employs Large Language Models as feature engineers. FeatLLM generates high-quality rules, significantly (10% on average) outperforming alternatives such as TabLLM and STUNT.
  arXiv  Detail & Related papers  (2024-04-15T06:26:08Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models [12.708117108874083]
  Large Language Models (LLMs) generate code snippets given natural language intents in zero-shot, i.e., without the need for specific fine-tuning. Previous research explored In-Context Learning (ICL) as a strategy to guide the LLM generative process with task-specific prompt examples. In this paper, we deliver a comprehensive study of. PEFT techniques for LLMs under the automated code generation scenario.
  arXiv  Detail & Related papers  (2023-08-21T04:31:06Z)
• Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs [2.9242435458494445]
  This paper presents an effective approach for boosting the performance of Code LLMs on low-resource languages using semi-synthetic data. We apply this approach to generate tens of thousands of validated training items for Julia, Lua, OCaml, R, and Racket.
  arXiv  Detail & Related papers  (2023-08-19T03:19:01Z)
• Scaling Sentence Embeddings with Large Language Models [43.19994568210206]
  In this work, we propose an in-context learning-based method aimed at improving sentence embeddings performance. Our approach involves adapting the previous prompt-based representation method for autoregressive models. By scaling model size, we find scaling to more than tens of billion parameters harms the performance on semantic textual similarity tasks.
  arXiv  Detail & Related papers  (2023-07-31T13:26:03Z)
• Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback [61.83548032416181]
  We present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
  arXiv  Detail & Related papers  (2023-07-29T18:01:46Z)
• Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models [75.75038268227554]
  Self-Checker is a framework comprising a set of plug-and-play modules that facilitate fact-checking. This framework provides a fast and efficient way to construct fact-checking systems in low-resource environments.
  arXiv  Detail & Related papers  (2023-05-24T01:46:07Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.