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LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (2304.01933v3)

Published 4 Apr 2023 in cs.CL

Abstract: The success of LLMs, like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e.g., ChatDoctor) or instruction data (e.g., Alpaca). Among the various fine-tuning methods, adapter-based parameter-efficient fine-tuning (PEFT) is undoubtedly one of the most attractive topics, as it only requires fine-tuning a few external parameters instead of the entire LLMs while achieving comparable or even better performance. To enable further research on PEFT methods of LLMs, this paper presents LLM-Adapters, an easy-to-use framework that integrates various adapters into LLMs and can execute these adapter-based PEFT methods of LLMs for different tasks. The framework includes state-of-the-art open-access LLMs such as LLaMA, BLOOM, and GPT-J, as well as widely used adapters such as Series adapters, Parallel adapter, Prompt-based learning and Reparametrization-based methods. Moreover, we conduct extensive empirical studies on the impact of adapter types, placement locations, and hyper-parameters to the best design for each adapter-based methods. We evaluate the effectiveness of the adapters on fourteen datasets from two different reasoning tasks, Arithmetic Reasoning and Commonsense Reasoning. The results demonstrate that using adapter-based PEFT in smaller-scale LLMs (7B) with few extra trainable parameters yields comparable, and in some cases superior, performance to powerful LLMs (175B) in zero-shot inference on both reasoning tasks.

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Citations (171)

Summary

  • The paper introduces a framework for parameter-efficient fine-tuning by integrating various adapters into LLMs, reducing resource demands while maintaining performance.
  • Empirical evaluations on 14 datasets reveal that optimized adapter placements, such as Series Adapters post-MLP layers and tuned LoRA settings, significantly enhance reasoning task accuracy.
  • The study demonstrates that adapter-based PEFT methods enable smaller models like LLaMA-13B to rival larger systems in arithmetic and commonsense reasoning, highlighting broader accessibility for resource-constrained environments.

LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of LLMs

The paper presents a comprehensive framework for parameter-efficient fine-tuning (PEFT) methods applied to LLMs. By introducing various adapters into LLMs, the framework seeks to optimize the fine-tuning process using fewer resources, while maintaining or even enhancing performance across different tasks. This essay explores the implementation details, empirical findings, and implications of utilizing adapter-based PEFT methods in practice.

Introduction to PEFT Methods and LLM-Adapters

PEFT methods have emerged as a compelling alternative to full-model fine-tuning (FFT), particularly for LLMs like GPT-4 and ChatGPT. Traditional FFT approaches are resource-intensive, requiring updates to all model parameters. In contrast, PEFT involves fine-tuning a smaller subset of parameters through the incorporation of adapters, thus offering a cost-effective and computationally efficient solution.

The LLM-Adapters framework integrates multiple adapter types, including Series adapters, Parallel adapters, Prompt-based learning, and Reparametrization-based methods. This allows researchers to leverage state-of-the-art open-source LLMs such as LLaMA, BLOOM, and GPT-J, and apply them to diverse reasoning tasks.

Adapter Architectures and Configurations

The paper categorizes PEFT methods into four main architectures, each offering unique mechanisms for fine-tuning:

  1. Prompt-based Learning: This includes methods like Prompt Tuning and Prefix Tuning, which involve adding trainable tensors to input embeddings or hidden states (Figure 1). Figure 1

    Figure 1: A detailed illustration of the model architectures of three different adapters: (a) Prefix-Tuning, (b) LoRA, (c) Series Adapter, and (d) Parallel Adapter.

  2. Reparametrization-based Methods: Techniques such as LoRA utilize low-rank transformations of network weights to reduce parameter count while maintaining performance.
  3. Series Adapters: These introduce additional learnable modules post-attention and FFN layers, configured using methods like Compacter or AdaMix.
  4. Parallel Adapters: These aim to integrate learnable components alongside distinct sublayers within the backbone model.

The paper explores optimal configurations, such as placement within MLP or Attention layers, and hyperparameters like the number of virtual tokens or low-rank matrix sizes.

Empirical Evaluation

The paper conducts extensive experiments on 14 datasets spanning Arithmetic and Commonsense Reasoning tasks. Results indicate that adapter-based PEFT with smaller-scale LLMs can achieve performance levels rivaling larger models like GPT-3.5 in certain scenarios.

Placement and Hyperparameter Analyses

Empirical findings reveal that:

  • For Series Adapters, best placement is after MLP layers, achieving significant accuracy improvements.
  • LoRA performs optimally when integrated into both the Attention and MLP layers.
  • Adjustments to hyperparameters, such as increasing LoRA rank from 8 to 32, can enhance model performance on reasoning tasks (Figure 2, Figure 3). Figure 2

    Figure 2: The average accuracy of different adapter locations on math reasoning datasets.

    Figure 3

Figure 3

Figure 3

Figure 3

Figure 3: The average accuracy of different variable settings on math reasoning datasets. Where "vt" refers to the number of virtual tokens, "bn" denotes the bottleneck size, while "r" is the LoRA rank.

Performance Outcomes

Adapter-based methods like LoRA can enable smaller LLMs, such as LLaMA-13B, to outperform larger models on specific tasks, notably in Arithmetic Reasoning datasets such as MultiArith and AddSub. In Commonsense Reasoning, setups like LLaMA-13B with Series or Parallel Adapters exhibit competitiveness with established models including ChatGPT.

Practical Implications and Future Directions

The framework underscores the potential for deploying LLMs in resource-constrained environments by using PEFT methods. LLM-Adapters make it feasible for researchers with limited computational budgets to explore advanced NLP applications, democratizing access to powerful LLM capabilities.

Moving forward, exploration into combining various adapters could lead to further performance gains across more complex tasks. Additionally, expanding evaluation to larger models, such as LLaMA-33B or LLaMA-65B, could provide insights into scaling effects and broader usability in diverse application domains.

Conclusion

The LLM-Adapters framework offers a versatile and efficient approach to fine-tuning LLMs with minimal computational overhead. Through meticulous evaluations and optimizations, the paper establishes that PEFT methods hold significant promise for maximizing performance in task-specific scenarios without the need for exhaustive resource investments. As AI models continue to evolve, such frameworks will be pivotal in broadening their applicability and impact across varied sectors.

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