Summary of Refining Joint Text and Source Code Embeddings For Retrieval Task with Parameter-efficient Fine-tuning, by Karim Galliamov et al.
Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning
by Karim Galliamov, Leila Khaertdinova, Karina Denisova
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a fine-tuning framework for transformer-based models in Natural Language Processing (NLP) that addresses the challenge of computational costs and time required for end-to-end fine-tuning as models increase in size. The approach leverages Parameter-Efficient Fine-Tuning (PEFT) techniques and contrastive learning objectives to improve bimodal representations learned by transformer models. Benchmarking PEFT methods is crucial, and this paper provides extensive benchmarking using the CodeT5+ model on two datasets. Experimental results demonstrate that the proposed framework can improve code-text retrieval performance by tuning only 0.4% parameters at most. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a big problem in Natural Language Processing (NLP) where big models take too long to train and use too much computer power. The researchers came up with a new way to fine-tune these models that uses less computational resources while still being effective. They tested this approach on two datasets using the CodeT5+ model, which is a type of transformer-based model. The results show that their method can improve code-text retrieval performance without requiring too many computational resources. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Nlp » Parameter efficient » Transformer
