MUSCLE: A Model Update Strategy for Compatible LLM Evolution
(2407.09435)Abstract
LLMs are frequently updated due to data or architecture changes to improve their performance. When updating models, developers often focus on increasing overall performance metrics with less emphasis on being compatible with previous model versions. However, users often build a mental model of the functionality and capabilities of a particular machine learning model they are interacting with. They have to adapt their mental model with every update -- a draining task that can lead to user dissatisfaction. In practice, fine-tuned downstream task adapters rely on pretrained LLM base models. When these base models are updated, these user-facing downstream task models experience instance regression or negative flips -- previously correct instances are now predicted incorrectly. This happens even when the downstream task training procedures remain identical. Our work aims to provide seamless model updates to a user in two ways. First, we provide evaluation metrics for a notion of compatibility to prior model versions, specifically for generative tasks but also applicable for discriminative tasks. We observe regression and inconsistencies between different model versions on a diverse set of tasks and model updates. Second, we propose a training strategy to minimize the number of inconsistencies in model updates, involving training of a compatibility model that can enhance task fine-tuned language models. We reduce negative flips -- instances where a prior model version was correct, but a new model incorrect -- by up to 40% from Llama 1 to Llama 2.
Overview
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The paper introduces a strategy called MUSCLE to maintain compatibility between different versions of LLMs, focusing on user experience and reducing regression when models are updated.
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Key contributions include new metrics for evaluating compatibility, a novel 'compatibility adapter' using knowledge distillation to align old and new model behaviors, and validation of the approach through extensive experiments across multiple LLMs and tasks.
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The results show up to 40% reduction in negative flips in some scenarios, indicating significant improvements in maintaining user-expected model performance across updates, with practical implications for user satisfaction and cognitive load.
MUSCLE: A Model Update Strategy for Compatible LLM Evolution
The paper "MUSCLE: A Model Update Strategy for Compatible LLM Evolution" addresses a crucial yet often overlooked challenge in the evolution of LLMs: maintaining compatibility between different versions of the models. This problem is significant as model updates typically focus on enhancing performance metrics without considering how changes might impact user experience, particularly for those who have developed a mental model of the LLM's capabilities and behaviors.
Key Contributions
The authors make several noteworthy contributions to the field:
- Compatibility Metrics: They introduce new evaluation metrics for measuring compatibility between different versions of LLMs. This includes extending the traditional negative flip rate (NFR) used for classification tasks to generative tasks as well. Metrics like backward trust compatibility (BTC) and negative flip impact are adapted to encompass both positive flips (correct to correct) and negative flips (correct to incorrect), as well as inconsistencies where both models are incorrect but change their predictions.
- Compatibility Adapter: The paper proposes a novel training strategy using a compatibility adapter. This adapter is fine-tuned to ensure minimal regression when updating from an older model version to a newer one. By leveraging knowledge distillation, the authors train the adapter to align with both the old and new models, significantly reducing negative flips by aligning more closely with the user's expectations.
- Experimental Validation: The authors validate their approach across a diverse set of tasks and models. They show that the compatibility adapter can reduce negative flips by up to 40% in some scenarios (e.g., from Llama 1 to Llama 2), without compromising the overall performance improvements brought by the new model versions.
Experimental Setup
The experiments are comprehensive, considering updates across multiple LLM families, including Llama and Vicuna, and evaluated on various downstream tasks like HellaSwag, PIQA, GSM8k, and SAMsum. For all tasks, parameter-efficient fine-tuning using Low-Rank Adapters (LoRA) is employed.
Numerical Results
The results are compelling:
- HellaSwag: A reduction in negative flip rate (NFR) by 40.60% (Llama 1 to Llama 2) and 38.74% (Vicuna 1.3 to Vicuna 1.5) while also achieving significant accuracy gains.
- PIQA: Similar improvements are noted with a reduction in NFR by 34.25% (Llama 1 to Llama 2).
- GSM8k: Up to 29% reduction in NFR when updated from Phi 1.5 to Phi 2, demonstrating the approach's utility in math reasoning tasks.
- SAMsum: For generative tasks, the compatibility adapter reduces ROUGE-1 score regression by 27.46% for Phi 1.5 to Phi 2 updates.
Theoretical and Practical Implications
The proposed methodology has significant theoretical and practical implications:
- User-Centric Model Updates: By maintaining compatibility, users can experience more consistent model behaviors, leading to improved satisfaction and reduced cognitive load.
- Extending to Generative Models: Extending compatibility metrics to generative models broadens the applicability of this approach, making it relevant for a larger array of tasks.
Speculations on Future Developments
This work paves the way for future research focused on achieving seamless model evolution. Key areas for future exploration might include:
- Tokenization and Vocabulary Changes: Investigating strategies to handle updates involving changes in tokenization or vocabulary size.
- Bias Mitigation: Ensuring that while compatibility is maintained, biases inherent in older models do not get perpetuated.
In conclusion, this paper provides a robust framework for addressing model compatibility in LLM updates, offering both a theoretical foundation and practical solutions for reducing negative flips and maintaining user trust during model evolution. The introduction of compatibility adapters and extended metrics represents a significant advance in the field, providing a more user-centered approach to LLM development and deployment.
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