Octavius: Mitigating Task Interference in Multimodal Large Language Models via LoRA-MoE

核心概念

Addressing task interference in multimodal learning with a novel framework, Octavius, integrating LoRA-MoE.

要約

Recent studies have shown the potential of Large Language Models (LLMs) in multimodal tasks. However, negative conflicts and interference can impact performance. Octavius proposes a framework combining MoE and LoRA to address this issue. The LoRA-MoE decoder allows for efficient involvement in various tasks and modalities. Experimental results show a 20% improvement across 2D and 3D downstream tasks. The Object-As-Scene point cloud encoder aligns scene-level features with language. Instruction tuning datasets like LAMM-v2 and Scan2Inst are used for training. Ablation studies on MoE architecture, gate routing, load balancing, and token-based gates provide insights into model performance enhancements.

統計

Experimental results show a 20% improvement across 2D and 3D downstream tasks.
The Object-As-Scene point cloud encoder aligns scene-level features with language.

引用

"LoRA-MoE combines MoE and LoRA to address task interference in multimodal learning."
"Experimental results demonstrate the effectiveness of Octavius in improving downstream tasks."

抽出されたキーインサイト

Octavius

by Zeren Chen,Z... 場所 arxiv.org 03-14-2024

https://arxiv.org/pdf/2311.02684.pdf

深掘り質問

How does the introduction of multiple modalities affect joint training performance?

The introduction of multiple modalities in joint training can have both positive and negative effects on performance. On one hand, incorporating different modalities like images and point clouds allows for a more comprehensive understanding of the data, enabling models to tackle complex tasks that require information from various sources. This can lead to improved generalization and robustness in handling diverse inputs.
However, the inclusion of multiple modalities also introduces challenges such as task interference, where learning different tasks simultaneously may hinder each other's optimization process. This phenomenon is exacerbated when there are limited annotated data available for fine-tuning across all modalities. The tug-of-war problem arises when conflicting objectives between tasks impede overall performance.
To address these challenges, frameworks like Octavius leverage techniques such as Mixture-of-Experts (MoE) combined with Parameter-Efficient Fine-Tuning (PEFT) strategies like LoRA to mitigate task interference and enhance model adaptability across various modalities during joint training.

How can load balancing strategies be effectively implemented in an instance-based gate scenario?

In an instance-based gate scenario where routing decisions are made based on individual instances rather than tokens or sequences, implementing load balancing strategies poses unique challenges. Traditional approaches like minimizing imbalanced weights among experts using auxiliary loss functions may not directly translate well into this context due to the specific nature of expert selection based on instances.
One way to effectively implement load balancing in an instance-based gate scenario is by considering the distribution of instances across different tasks or modalities within the dataset. By ensuring a balanced representation of instances for each task during training, it becomes easier to achieve fair allocation of expertise among experts without bias towards certain types of data points.
Additionally, designing adaptive mechanisms that dynamically adjust routing scores based on the diversity and complexity of instances encountered during inference can help maintain equilibrium among experts while addressing any potential imbalance issues that may arise during model operation.
By carefully calibrating routing mechanisms with insights from dataset characteristics and task requirements, load balancing strategies can be tailored to suit instance-based gating scenarios effectively within multimodal learning frameworks like Octavius.

What are the implications of using an instance-based gate routing strategy over token-based gates?

Using an instance-based gate routing strategy offers several advantages over traditional token-based gates in multimodal learning scenarios:

Task-specific Expertise: Instance-based gating allows models to assign dedicated experts for specific knowledge acquisition based on individual instances' characteristics. This enables better alignment between tasks and modality-specific features.

Reduced Interference: By sparsely activating independent experts according to input instructions at an instance level, interference between distinct granularities or domains is mitigated more effectively compared to token-level gating.

Enhanced Generalizability: Instance-level gating promotes better adaptation capabilities by tailoring responses at a finer granularity than traditional token-level approaches.

Efficient Resource Utilization: Instance-specific routing optimizes resource utilization by focusing computational efforts only on relevant aspects per input rather than processing entire sequences uniformly.

5 .Improved Performance Stability: The dynamic nature of instance-gate routing ensures flexible adjustments accordingto varying input contexts or complexities,reducing relianceon fixed patternsor pre-defined structures inherentin statictoken-gating schemes.
Overall,the useofinstance-bas edgate routings trategy enhancesmodel flexibilityandadaptabilitywhileaddressingtaskinterferenceissuesmoreeffectivelyinmultimod allearningenvironmentslikeOctavius