Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation

• URL: http://arxiv.org/abs/2207.03509v1
• Date: Thu, 7 Jul 2022 18:00:22 GMT
• Title: Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation
• Authors: Zejiang Hou, Julian Salazar, George Polovets
• Abstract summary: Large pretrained language models (PLMs) are often domain- or task-adapted via fine-tuning or prompting. Instead, we prepare PLMs for data- and parameter-efficient adaptation by learning to learn the difference between general and adapted PLMs.
• Score: 11.960178399478718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large pretrained language models (PLMs) are often domain- or task-adapted via fine-tuning or prompting. Finetuning requires modifying all of the parameters and having enough data to avoid overfitting while prompting requires no training and few examples but limits performance. Instead, we prepare PLMs for data- and parameter-efficient adaptation by learning to learn the difference between general and adapted PLMs. This difference is expressed in terms of model weights and sublayer structure through our proposed dynamic low-rank reparameterization and learned architecture controller. Experiments on few-shot dialogue completion, low-resource abstractive summarization, and multi-domain language modeling show improvements in adaptation time and performance over direct finetuning or preparation via domain-adaptive pretraining. Ablations show our task-adaptive reparameterization (TARP) and model search (TAMS) components individually improve on other parameter-efficient transfer like adapters and structure-learning methods like learned sparsification.

Related papers

• Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods [69.36397993451742]
  This work introduces Context-aware Prompt Tuning (CPT), a method inspired by ICL, PT, and adversarial attacks. We modify specific context tokens, considering the unique structure of input and output formats. Inspired by adversarial attacks, we adjust the input based on the labels present in the context, focusing on minimizing, rather than maximizing, the loss.
  arXiv  Detail & Related papers  (2024-10-22T17:45:47Z)
• 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)
• Parameter-Efficient Fine-Tuning With Adapters [5.948206235442328]
  This research introduces a novel adaptation method utilizing the UniPELT framework as a base. Our method employs adapters, which enable efficient transfer of pretrained models to new tasks with minimal retraining of the base model parameters.
  arXiv  Detail & Related papers  (2024-05-09T01:40:38Z)
• Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning [12.648711621637663]
  This paper introduces a novel. COCO-Efficient Fine-Tuning (PEFT) framework for multi-modal, multi-task transfer learning with pre-trained language models. We propose Context-PEFT, which learns different groups of adaptor parameters based on the token's domain. Our method is evaluated on the captioning task, where it outperforms full fine-tuning under similar data constraints.
  arXiv  Detail & Related papers  (2023-12-14T13:00:24Z)
• On Conditional and Compositional Language Model Differentiable Prompting [75.76546041094436]
  Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks. We propose a new model, Prompt Production System (PRopS), which learns to transform task instructions or input metadata, into continuous prompts.
  arXiv  Detail & Related papers  (2023-07-04T02:47:42Z)
• PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation [61.05254852400895]
  Parse-Instructed Prefix (PIP) is a novel adaptation of prefix-tuning to tune large pre-trained language models. In contrast to traditional fine-tuning methods for this task, PIP is a compute-efficient alternative with 10 times less learnable parameters.
  arXiv  Detail & Related papers  (2023-05-26T07:42:38Z)
• TADA: Efficient Task-Agnostic Domain Adaptation for Transformers [3.9379577980832843]
  In this work, we introduce TADA, a novel task-agnostic domain adaptation method. Within TADA, we retrain embeddings to learn domain-aware input representations and tokenizers for the transformer encoder. We conduct experiments with meta-embeddings and newly introduced meta-tokenizers, resulting in one model per task in multi-domain use cases.
  arXiv  Detail & Related papers  (2023-05-22T04:53:59Z)
• Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models [137.74524357614285]
  We introduce a novel Gradient-RegulAted Meta-prompt learning framework. It helps pre-training models adapt to downstream tasks in a parameter -- and data -- efficient way. GRAM can be easily incorporated into various prompt tuning methods in a model-agnostic way.
  arXiv  Detail & Related papers  (2023-03-12T05:03:37Z)
• Rethinking Efficient Tuning Methods from a Unified Perspective [34.67645496324432]
  We revisit the design paradigm of PETL and derive a unified framework U-Tuning for parameter-efficient transfer learning. The U-Tuning framework can simultaneously encompass existing methods and derive new approaches for parameter-efficient transfer learning.
  arXiv  Detail & Related papers  (2023-03-01T17:38:03Z)
• Towards a Unified View of Parameter-Efficient Transfer Learning [108.94786930869473]
  Fine-tuning large pre-trained language models on downstream tasks has become the de-facto learning paradigm in NLP. Recent work has proposed a variety of parameter-efficient transfer learning methods that only fine-tune a small number of (extra) parameters to attain strong performance. We break down the design of state-of-the-art parameter-efficient transfer learning methods and present a unified framework that establishes connections between them.
  arXiv  Detail & Related papers  (2021-10-08T20:22:26Z)

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.