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Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models

https://doi.org/10.1145/3714461
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10664-024-10457-5openalex-snapshot
Utilization of pre-trained language models for adapter-based knowledge transfer in software engineering
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GPT detectors are biased against non-native English writers
inferredcites →https://doi.org/10.1038/s42256-023-00626-4openalex-snapshot
Parameter-efficient fine-tuning of large-scale pre-trained language models
inferredcites →https://doi.org/10.1109/ase56229.2023.00125openalex-snapshot
An Empirical Study of Parameter-Efficient Fine-Tuning Methods for Pre-Trained Code Models
inferredcites →https://doi.org/10.1109/cvpr52729.2023.01438openalex-snapshot
Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering
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inferredcites →https://doi.org/10.1109/icse48619.2023.00013openalex-snapshot
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inferredcites →https://doi.org/10.1109/icse48619.2023.00129openalex-snapshot
Automated Program Repair in the Era of Large Pre-trained Language Models
inferredcites →https://doi.org/10.1109/icse48619.2023.00205openalex-snapshot
Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning
inferredcites →https://doi.org/10.1109/icsme52107.2021.00044openalex-snapshot
Assessing Generalizability of CodeBERT
inferredcites →https://doi.org/10.1109/saner60148.2024.00055openalex-snapshot
Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
inferredcites →https://doi.org/10.1109/tse.2023.3267446openalex-snapshot
MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation
inferredcites →https://doi.org/10.1145/3196398.3196408openalex-snapshot
Learning to mine aligned code and natural language pairs from stack overflow
inferredcites →https://doi.org/10.1145/3491101.3519665openalex-snapshot
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models
inferredcites →https://doi.org/10.1145/3510455.3512771openalex-snapshot
Better modeling the programming world with code concept graphs-augmented multi-modal learning
inferredcites →https://doi.org/10.1145/3520312.3534862openalex-snapshot
A systematic evaluation of large language models of code
inferredcites →https://doi.org/10.1145/3524459.3527351openalex-snapshot
Can OpenAI's codex fix bugs?
inferredcites →https://doi.org/10.1145/3524610.3527892openalex-snapshot
On the cross-modal transfer from natural language to code through adapter modules
inferredcites →https://doi.org/10.1145/3540250.3549101openalex-snapshot
Less training, more repairing please: revisiting automated program repair via zero-shot learning
inferredcites →https://doi.org/10.1145/3540250.3549113openalex-snapshot
No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence
inferredcites →https://doi.org/10.1145/3597503.3608134openalex-snapshot
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
inferredcites →https://doi.org/10.1145/3605943openalex-snapshot
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
inferredcites →https://doi.org/10.1145/3611643.3616244openalex-snapshot
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
inferredcites →https://doi.org/10.1145/3611643.3616256openalex-snapshot
RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair
inferredcites →https://doi.org/10.1162/tacl_a_00577openalex-snapshot
Efficient Methods for Natural Language Processing: A Survey
inferredcites →https://doi.org/10.1609/aaai.v34i05.6430openalex-snapshot
TreeGen: A Tree-Based Transformer Architecture for Code Generation
inferredcites →https://doi.org/10.1609/aaai.v37i4.25642openalex-snapshot
Repair Is Nearly Generation: Multilingual Program Repair with LLMs
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-demos.6openalex-snapshot
Transformers: State-of-the-Art Natural Language Processing
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.139openalex-snapshot
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.18653/v1/2021.acl-short.98openalex-snapshot
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.243openalex-snapshot
The Power of Scale for Parameter-Efficient Prompt Tuning
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.685openalex-snapshot
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2021.findings-emnlp.232openalex-snapshot
Retrieval Augmented Code Generation and Summarization
inferredcites →https://doi.org/10.18653/v1/2022.naacl-main.167openalex-snapshot
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?
inferredcites →https://doi.org/10.18653/v1/2022.naacl-main.201openalex-snapshot
MetaICL: Learning to Learn In Context
inferredcites →https://doi.org/10.18653/v1/2023.emnlp-main.68openalex-snapshot
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2023.findings-acl.325openalex-snapshot
CodePrompt: Task-Agnostic Prefix Tuning for Program and Language Generation
inferredcites →https://doi.org/10.18653/v1/p19-1355openalex-snapshot
Energy and Policy Considerations for Deep Learning in NLP
inferredcites →https://doi.org/10.48550/arxiv.2005.11401openalex-snapshot
Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems
inferredcites →https://doi.org/10.48550/arxiv.2203.02155openalex-snapshot
Discernment and Social Learning as a Companion Training Layer
inferredcites →https://doi.org/10.52202/068431-0142openalex-snapshot
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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