PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning
- URL: http://arxiv.org/abs/2412.04975v1
- Date: Fri, 06 Dec 2024 11:49:18 GMT
- Title: PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning
- Authors: Jonas Rieger, Mattes Ruckdeschel, Gregor Wiedemann,
- Abstract summary: Few-shot learning and parameter-efficient fine-tuning are crucial to overcome the challenges of data scarcity and ever growing language model sizes.
We propose PETapter, a novel method that effectively combines PEFT methods with PET-style classification heads to boost few-shot learning capabilities.
We show that PETapter achieves comparable performance to full few-shot fine-tuning using pattern-exploiting training (PET)
- Score: 1.0541541376305243
- License:
- Abstract: Few-shot learning and parameter-efficient fine-tuning (PEFT) are crucial to overcome the challenges of data scarcity and ever growing language model sizes. This applies in particular to specialized scientific domains, where researchers might lack expertise and resources to fine-tune high-performing language models to nuanced tasks. We propose PETapter, a novel method that effectively combines PEFT methods with PET-style classification heads to boost few-shot learning capabilities without the significant computational overhead typically associated with full model training. We validate our approach on three established NLP benchmark datasets and one real-world dataset from communication research. We show that PETapter not only achieves comparable performance to full few-shot fine-tuning using pattern-exploiting training (PET), but also provides greater reliability and higher parameter efficiency while enabling higher modularity and easy sharing of the trained modules, which enables more researchers to utilize high-performing NLP-methods in their research.
Related papers
- Enhancing SLM via ChatGPT and Dataset Augmentation [0.3844771221441211]
We employ knowledge distillation-based techniques and synthetic dataset augmentation to bridge the performance gap between large language models (LLMs) and small language models (SLMs)
Our methods involve two forms of rationale generation--information extraction and informed reasoning--to enrich the ANLI dataset.
Our findings reveal that the incorporation of synthetic rationales significantly improves the model's ability to comprehend natural language, leading to 1.3% and 2.3% higher classification accuracy, respectively, on the ANLI dataset.
arXiv Detail & Related papers (2024-09-19T09:24:36Z) - Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review [50.78587571704713]
Learn-Focus-Review (LFR) is a dynamic training approach that adapts to the model's learning progress.
LFR tracks the model's learning performance across data blocks (sequences of tokens) and prioritizes revisiting challenging regions of the dataset.
Compared to baseline models trained on the full datasets, LFR consistently achieved lower perplexity and higher accuracy.
arXiv Detail & Related papers (2024-09-10T00:59:18Z) - Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R [1.9799527196428242]
We evaluate PEFT methods, LoRA, Compacter, and IA3 on Large Language Models for code summarization and generation.
Our experiments reveal that LoRA consistently outperforms Compacter and IA3 in all settings.
Our study can direct future research in developing code intelligent tasks for unseen languages including R.
arXiv Detail & Related papers (2024-03-16T03:12:45Z) - Retrieval-based Knowledge Transfer: An Effective Approach for Extreme
Large Language Model Compression [64.07696663255155]
Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks.
However, the massive size of these models poses huge challenges for their deployment in real-world applications.
We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
arXiv Detail & Related papers (2023-10-24T07:58:20Z) - Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised
Language Understanding [38.11411155621616]
We study self-training as one of the predominant semi-supervised learning approaches.
We present UPET, a novel Uncertainty-aware self-Training framework.
We show that UPET achieves a substantial improvement in terms of performance and efficiency.
arXiv Detail & Related papers (2023-10-19T02:18:29Z) - ConPET: Continual Parameter-Efficient Tuning for Large Language Models [65.48107393731861]
Continual learning requires continual adaptation of models to newly emerging tasks.
We propose Continual.
Efficient Tuning (ConPET), a generalizable paradigm for.
continual task adaptation of large language models.
arXiv Detail & Related papers (2023-09-26T08:52:04Z) - Parameter and Computation Efficient Transfer Learning for
Vision-Language Pre-trained Models [79.34513906324727]
In this paper, we aim at parameter and efficient transfer learning (PCETL) for vision-language pre-trained models.
We propose a novel dynamic architecture skipping (DAS) approach towards effective PCETL.
arXiv Detail & Related papers (2023-09-04T09:34:33Z) - Exploring the Impact of Model Scaling on Parameter-Efficient Tuning [100.61202305296275]
Scaling-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs)
In small PLMs, there are usually noticeable performance differences among PET methods.
We introduce a more flexible PET method called Arbitrary PET (APET) method.
arXiv Detail & Related papers (2023-06-04T10:10:54Z) - Neural Architecture Search for Parameter-Efficient Fine-tuning of Large
Pre-trained Language Models [25.33932250843436]
We propose an efficient NAS method for learning PET architectures via structured and unstructured pruning.
We present experiments on GLUE demonstrating the effectiveness of our algorithm and discuss how PET architectural design choices affect performance in practice.
arXiv Detail & Related papers (2023-05-26T03:01:07Z) - MoEfication: Conditional Computation of Transformer Models for Efficient
Inference [66.56994436947441]
Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost.
We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon.
We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
arXiv Detail & Related papers (2021-10-05T02:14:38Z)
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.