Divergent Token Metrics: A Novel Approach to Evaluating Compressed Large Language Models

Temel Kavramlar

Divergent Token Metrics (DTMs) provide a more nuanced and accurate evaluation of compressed Large Language Models (LLMs) compared to traditional perplexity or accuracy measures, enabling deeper insights into the impacts of individual model components during the compression process.

Özet

The paper introduces Divergent Token Metrics (DTMs), a novel approach to assessing the performance of compressed LLMs. Traditional metrics like perplexity or accuracy fail to capture the subtle nuances in text generation quality introduced by compression.
The key highlights are:

DTMs, including the First Divergent Token Metric (FDTM) and the Share of Divergent Tokens Metric (SDTM), directly measure the divergence between the outputs of the original and compressed models during the iterative generation process. This provides a more accurate reflection of the actual text generation quality.

Using FDTM for model sparsification, the authors show that 25% of all attention components in the Llama-2 model family can be pruned beyond 90% while still maintaining state-of-the-art performance.

For model quantization, FDTM suggests that more than 80% of the parameters can be naively converted to int8 without special outlier management, outperforming standard techniques like LLM.int8().

The proposed metrics reveal that the attention mechanism is not utilized efficiently throughout the model, with a significant portion of attention components being highly prunable. This suggests the need for more targeted compression strategies that consider the individual importance of model components.

Overall, the Divergent Token Metrics provide a more comprehensive and accurate assessment of compressed LLMs, enabling the development of more efficient and effective compression techniques.

İstatistikler

25% of attention components can be pruned beyond 90% while maintaining SOTA performance.
More than 80% of model parameters can be converted to int8 without special outlier management.

Alıntılar

"Perplexity fails to identify minor variations in model degradation at an early stage."
"Divergent Token Metrics closely reflect the generation process and so can be a measure to foster confidence in the deployed compressed models."

Önemli Bilgiler Şuradan Elde Edildi

Divergent Token Metrics

by Björ... : arxiv.org 04-04-2024

https://arxiv.org/pdf/2311.01544.pdf

Daha Derin Sorular

How can the proposed Divergent Token Metrics be extended to evaluate the preservation of specific model capabilities, such as safety alignment, during the compression process?

The Divergent Token Metrics (DTMs) can be extended to evaluate the preservation of specific model capabilities, such as safety alignment, during the compression process by incorporating additional token-based metrics that focus on the specific aspects of model behavior that need to be preserved. For safety alignment, the metrics can be designed to assess the model's ability to maintain ethical considerations, avoid biases, or adhere to specific guidelines during the compression process. These metrics can measure the divergence in model behavior related to safety alignment, such as the frequency of potentially harmful outputs, the consistency in ethical decision-making, or the alignment with predefined safety constraints.
Furthermore, the DTMs can be tailored to include probes that specifically target safety-related components or functionalities within the model. By analyzing the divergence in these components using metrics like the First Divergent Token Metric (FDTM) or Share of Divergent Tokens Metric (SDTM), it becomes possible to quantify the impact of compression on safety alignment. The metrics can provide insights into which parts of the model are most sensitive to compression in terms of safety considerations and guide the development of targeted compression strategies that prioritize the preservation of safety alignment.
In summary, extending the Divergent Token Metrics to evaluate the preservation of specific model capabilities like safety alignment involves designing specialized metrics and probes that focus on safety-related aspects of the model and analyzing the divergence in these areas during the compression process.

How can the proposed Divergent Token Metrics be extended to evaluate the preservation of specific model capabilities, such as safety alignment, during the compression process?

The Divergent Token Metrics (DTMs) can be extended to evaluate the preservation of specific model capabilities, such as safety alignment, during the compression process by incorporating additional token-based metrics that focus on the specific aspects of model behavior that need to be preserved. For safety alignment, the metrics can be designed to assess the model's ability to maintain ethical considerations, avoid biases, or adhere to specific guidelines during the compression process. These metrics can measure the divergence in model behavior related to safety alignment, such as the frequency of potentially harmful outputs, the consistency in ethical decision-making, or the alignment with predefined safety constraints.
Furthermore, the DTMs can be tailored to include probes that specifically target safety-related components or functionalities within the model. By analyzing the divergence in these components using metrics like the First Divergent Token Metric (FDTM) or Share of Divergent Tokens Metric (SDTM), it becomes possible to quantify the impact of compression on safety alignment. The metrics can provide insights into which parts of the model are most sensitive to compression in terms of safety considerations and guide the development of targeted compression strategies that prioritize the preservation of safety alignment.
In summary, extending the Divergent Token Metrics to evaluate the preservation of specific model capabilities like safety alignment involves designing specialized metrics and probes that focus on safety-related aspects of the model and analyzing the divergence in these areas during the compression process.

How can the proposed Divergent Token Metrics be extended to evaluate the preservation of specific model capabilities, such as safety alignment, during the compression process?

The Divergent Token Metrics (DTMs) can be extended to evaluate the preservation of specific model capabilities, such as safety alignment, during the compression process by incorporating additional token-based metrics that focus on the specific aspects of model behavior that need to be preserved. For safety alignment, the metrics can be designed to assess the model's ability to maintain ethical considerations, avoid biases, or adhere to specific guidelines during the compression process. These metrics can measure the divergence in model behavior related to safety alignment, such as the frequency of potentially harmful outputs, the consistency in ethical decision-making, or the alignment with predefined safety constraints.
Furthermore, the DTMs can be tailored to include probes that specifically target safety-related components or functionalities within the model. By analyzing the divergence in these components using metrics like the First Divergent Token Metric (FDTM) or Share of Divergent Tokens Metric (SDTM), it becomes possible to quantify the impact of compression on safety alignment. The metrics can provide insights into which parts of the model are most sensitive to compression in terms of safety considerations and guide the development of targeted compression strategies that prioritize the preservation of safety alignment.
In summary, extending the Divergent Token Metrics to evaluate the preservation of specific model capabilities like safety alignment involves designing specialized metrics and probes that focus on safety-related aspects of the model and analyzing the divergence in these areas during the compression process.