BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-task Large Language Models

• URL: http://arxiv.org/abs/2407.15857v1
• Date: Mon, 8 Jul 2024 06:38:50 GMT
• Title: BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-task Large Language Models
• Authors: Simen Eide, Arnoldo Frigessi,
• Abstract summary: This paper introduces Bayesian Hierarchical Low-Rank Adaption (BoRA), a novel method for finetuning multi-task Large Language Models (LLMs) BoRA addresses trade-offs by leveraging a Bayesian hierarchical model that allows tasks to share information through global hierarchical priors. Our experimental results show that BoRA outperforms both individual and unified model approaches, achieving lower perplexity and better generalization across tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Bayesian Hierarchical Low-Rank Adaption (BoRA), a novel method for finetuning multi-task Large Language Models (LLMs). Current finetuning approaches, such as Low-Rank Adaption (LoRA), perform exeptionally well in reducing training parameters and memory usage but face limitations when applied to multiple similar tasks. Practitioners usually have to choose between training separate models for each task or a single model for all tasks, both of which come with trade-offs in specialization and data utilization. BoRA addresses these trade-offs by leveraging a Bayesian hierarchical model that allows tasks to share information through global hierarchical priors. This enables tasks with limited data to benefit from the overall structure derived from related tasks while allowing tasks with more data to specialize. Our experimental results show that BoRA outperforms both individual and unified model approaches, achieving lower perplexity and better generalization across tasks. This method provides a scalable and efficient solution for multi-task LLM finetuning, with significant practical implications for diverse applications.

Related papers

• MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning [74.43869839954168]
  We propose MTL-LoRA, which retains the advantages of low-rank adaptation while significantly enhancing multi-task learning capabilities. MTL-LoRA augments LoRA by incorporating additional task-adaptive parameters that differentiate task-specific information. This approach enables large language models (LLMs) pre-trained on general corpus to adapt to different target task domains with a limited number of trainable parameters.
  arXiv  Detail & Related papers  (2024-10-12T08:32:26Z)
• On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion [23.63688816017186]
  Existing weak-to-strong methods often employ a static knowledge transfer ratio and a single small model for transferring complex knowledge. We propose a dynamic logit fusion approach that works with a series of task-specific small models, each specialized in a different task. Our method closes the performance gap by 96.4% in single-task scenarios and by 86.3% in multi-task scenarios.
  arXiv  Detail & Related papers  (2024-06-17T03:07:41Z)
• Multimodal Instruction Tuning with Conditional Mixture of LoRA [54.65520214291653]
  This paper introduces a novel approach that integrates multimodal instruction tuning with Low-Rank Adaption (LoRA) It innovates upon LoRA by dynamically constructing low-rank adaptation matrices tailored to the unique demands of each input instance. Experimental results on various multimodal evaluation datasets indicate that MixLoRA not only outperforms the conventional LoRA with the same or even higher ranks.
  arXiv  Detail & Related papers  (2024-02-24T20:15:31Z)
• PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation [65.268245109828]
  We introduce PRILoRA, which linearly allocates a different rank for each layer, in an increasing manner, and performs pruning throughout the training process. We validate the effectiveness of PRILoRA through extensive experiments on eight GLUE benchmarks, setting a new state of the art.
  arXiv  Detail & Related papers  (2024-01-20T20:25:17Z)
• Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion [86.6191592951269]
  Merging models fine-tuned from common extensively pretrained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multitask model that performs well across diverse tasks. We propose the CONtinuous relaxation dis (Concrete) subspace learning method to identify a common lowdimensional subspace and utilize its shared information track interference problem without sacrificing performance.
  arXiv  Detail & Related papers  (2023-12-11T07:24:54Z)
• OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models [72.8156832931841]
  Generalist models are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model. We release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction.
  arXiv  Detail & Related papers  (2022-12-08T17:07:09Z)
• Task Adaptive Parameter Sharing for Multi-Task Learning [114.80350786535952]
  Adaptive Task Adapting Sharing (TAPS) is a method for tuning a base model to a new task by adaptively modifying a small, task-specific subset of layers. Compared to other methods, TAPS retains high accuracy on downstream tasks while introducing few task-specific parameters. We evaluate our method on a suite of fine-tuning tasks and architectures (ResNet, DenseNet, ViT) and show that it achieves state-of-the-art performance while being simple to implement.
  arXiv  Detail & Related papers  (2022-03-30T23:16:07Z)
• Rethinking Hard-Parameter Sharing in Multi-Task Learning [20.792654758645302]
  Hard parameter sharing in multi-task learning (MTL) allows tasks to share some of model parameters, reducing storage cost and improving prediction accuracy. The common sharing practice is to share bottom layers of a deep neural network among tasks while using separate top layers for each task. Using separate bottom-layer parameters could achieve significantly better performance than the common practice.
  arXiv  Detail & Related papers  (2021-07-23T17:26:40Z)

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.