Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning

• URL: http://arxiv.org/abs/2402.08594v1
• Date: Tue, 13 Feb 2024 16:57:02 GMT
• Title: Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
• Authors: Haeju Lee, Minchan Jeong, Se-Young Yun, Kee-Eung Kim
• Abstract summary: We argue that when we extract knowledge from source tasks via training source prompts, we need to consider this correlation among source tasks for better transfer to target tasks. We propose a Bayesian approach where we work with the posterior distribution of prompts across source tasks. We show extensive experimental results on the standard benchmark NLP tasks, where our Bayesian multi-task transfer learning approach outperforms the state-of-the-art methods in many settings.
• Score: 44.43258626098661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt tuning, in which prompts are optimized to adapt large-scale pre-trained language models to downstream tasks instead of fine-tuning the full model parameters, has been shown to be particularly effective when the prompts are trained in a multi-task transfer learning setting. These methods generally involve individually training prompts for each source task and then aggregating them to provide the initialization of the prompt for the target task. However, this approach critically ignores the fact that some of the source tasks could be negatively or positively interfering with each other. We argue that when we extract knowledge from source tasks via training source prompts, we need to consider this correlation among source tasks for better transfer to target tasks. To this end, we propose a Bayesian approach where we work with the posterior distribution of prompts across source tasks. We obtain representative source prompts corresponding to the samples from the posterior utilizing Stein Variational Gradient Descent, which are then aggregated to constitute the initial target prompt. We show extensive experimental results on the standard benchmark NLP tasks, where our Bayesian multi-task transfer learning approach outperforms the state-of-the-art methods in many settings. Furthermore, our approach requires no auxiliary models other than the prompt itself, achieving a high degree of parameter efficiency.

Related papers

• Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition [0.0]
  Multi-task prompt tuning has garnered considerable attention for its inherent modularity and potential to enhance parameter-efficient transfer learning. This paper aims to analyze and improve the performance of multiple tasks by facilitating the transfer of knowledge between their corresponding prompts in a multi-task setting.
  arXiv  Detail & Related papers  (2024-08-23T17:01:51Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Identification of Negative Transfers in Multitask Learning Using Surrogate Models [29.882265735630046]
  Multitask learning is widely used to train a low-resource target task by augmenting it with multiple related source tasks. A critical problem in multitask learning is identifying subsets of source tasks that would benefit the target task. We introduce an efficient procedure to address this problem via surrogate modeling.
  arXiv  Detail & Related papers  (2023-03-25T23:16:11Z)
• Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning [43.639430661322585]
  We propose multitask prompt tuning (MPT) MPT learns a single transferable prompt by distilling knowledge from multiple task-specific source prompts. We then learn multiplicative low rank updates to this shared prompt to efficiently adapt it to each downstream target task.
  arXiv  Detail & Related papers  (2023-03-06T03:25:59Z)
• Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning [85.55727213502402]
  We focus on improving the few-shot performance of prompt tuning by transferring knowledge from soft prompts of source tasks. We propose Sample-specific Ensemble of Source Models (SESoM) SESoM learns to adjust the contribution of each source model for each target sample separately when ensembling source model outputs.
  arXiv  Detail & Related papers  (2022-10-23T01:33:16Z)
• Instance-wise Prompt Tuning for Pretrained Language Models [72.74916121511662]
  Instance-wise Prompt Tuning (IPT) is the first prompt learning paradigm that injects knowledge from the input data instances to the prompts. IPT significantly outperforms task-based prompt learning methods, and achieves comparable performance to conventional finetuning with only 0.5% - 1.5% of tuned parameters.
  arXiv  Detail & Related papers  (2022-06-04T10:08:50Z)
• Provable Benefits of Representational Transfer in Reinforcement Learning [59.712501044999875]
  We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation. We show that given generative access to source tasks, we can discover a representation, using which subsequent linear RL techniques quickly converge to a near-optimal policy.
  arXiv  Detail & Related papers  (2022-05-29T04:31:29Z)
• Active Multi-Task Representation Learning [50.13453053304159]
  We give the first formal study on resource task sampling by leveraging the techniques from active learning. We propose an algorithm that iteratively estimates the relevance of each source task to the target task and samples from each source task based on the estimated relevance.
  arXiv  Detail & Related papers  (2022-02-02T08:23:24Z)
• SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer [7.2462572989580405]
  We propose a novel prompt-based transfer learning approach called SPoT: Soft Prompt Transfer. We show SPoT significantly boosts the performance of PromptTuning across many tasks. We also conduct a large-scale study on task transferability with 26 NLP tasks and 160 combinations of source-target tasks.
  arXiv  Detail & Related papers  (2021-10-15T07:35:58Z)

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.