Leveraging Diverse Language Models through Perplexity-Guided Fusion at Test-Time

Core Concepts

PackLLM performs an effective fusion of arbitrary user-specified Large Language Models (LLMs) at test-time by optimizing the importance weights of each LLM based on the input prompt's perplexity.

Abstract

The paper introduces Pack of LLMs (PackLLM), a test-time fusion method that combines the knowledge of arbitrary user-specified LLMs during inference. PackLLM does not require any training of fusion models and can leverage newly released LLMs. The key insights are: PackLLM formulates the fusion problem as an optimization that minimizes the perplexity of the input prompt over the combined LLMs. This allows PackLLM to effectively leverage the expertise of each LLM. PackLLMsim is a simple perplexity-based weighting scheme that validates the use of perplexity as a reliable indicator of LLM expertise. PackLLMopt approximately solves the perplexity minimization problem via a greedy algorithm, deriving the importance weights for each LLM. Experiments show that PackLLM outperforms test-time fusion baselines by 1.72-1.89% accuracy points on average across 25 diverse tasks. It can also leverage newly released LLMs to outperform learning-based fusion approaches by 3.92-11.94% accuracy points. The paper demonstrates the effectiveness of PackLLM in fusing diverse LLMs at test-time without any additional training, making it a modular and scalable approach compared to learning-based fusion methods.

Stats

Perplexity is a reliable metric for measuring each LLM's expertise on the input prompt. PackLLM outperforms test-time fusion baselines by 1.72-1.89% accuracy points on average across 25 diverse tasks. PackLLM can leverage newly released LLMs to outperform learning-based fusion approaches by 3.92-11.94% accuracy points.

Quotes

"PackLLM does not require any training of fusion modules, while it can combine arbitrary user-specified LLMs during inference." "PackLLM minimizes the perplexity over the input prompt, so that the fused LLM understands the task better." "Experimental results show that (i) perplexity is a reliable measure for LLM fusion, (ii) PackLLM outperforms test-time fusion baselines by 1.89% accuracy points, and (iii) PackLLM can leverage new LLMs to improve performance over learning-based fusion approaches by 3.92–11.94% accuracy points."

Key Insights Distilled From

Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization

by Costas Mavro... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11531.pdf

Deeper Inquiries

How can PackLLM be extended to handle LLMs with different vocabularies more effectively?

PackLLM can be extended to handle LLMs with different vocabularies more effectively by implementing a more robust tokenizer selection and alignment strategy. This strategy should focus on mapping tokens from different vocabularies to a common vocabulary to ensure seamless integration of LLMs with varying tokenizers. By utilizing advanced techniques like Minimum Edit Distance (MinED) for token mapping and alignment, PackLLM can effectively combine the output logits of LLMs with different vocabularies. Additionally, incorporating techniques for vocabulary expansion or adaptation could further enhance the compatibility of LLMs with diverse vocabularies.

What are the potential limitations of using perplexity as the sole criterion for determining LLM importance weights?

While perplexity is a valuable metric for measuring a language model's understanding of a given input prompt, using it as the sole criterion for determining LLM importance weights may have some limitations. One potential limitation is that perplexity may not capture the full spectrum of a model's expertise, as it primarily focuses on the model's ability to predict the next token in a sequence. This narrow focus may overlook other aspects of a model's performance, such as semantic understanding or contextual relevance. Another limitation is that perplexity may not account for the complexity or specificity of the task at hand. Different tasks may require different levels of expertise or domain knowledge, which perplexity alone may not fully capture. Additionally, perplexity may be sensitive to the length and structure of the input prompt, potentially leading to biased weight assignments based on prompt characteristics rather than true model expertise. Furthermore, perplexity-based weighting may not be robust to noise or outliers in the input data, potentially leading to suboptimal weight assignments. It is essential to consider these limitations and potentially complement perplexity with other metrics or criteria to ensure a more comprehensive and accurate determination of LLM importance weights.

How could PackLLM be adapted to handle dynamic changes in the available LLMs during inference, rather than relying on a fixed set of models?

To adapt PackLLM to handle dynamic changes in the available LLMs during inference, a flexible and scalable approach is needed. One way to achieve this is by implementing a dynamic model selection mechanism that can incorporate new LLMs on-the-fly. This mechanism could involve periodically updating the set of available LLMs based on new releases or performance evaluations. Additionally, PackLLM could leverage meta-learning techniques to adapt to new LLMs by quickly learning the optimal fusion strategy for a given set of models. By continuously evaluating the performance of new LLMs and updating the fusion weights based on their expertise, PackLLM can dynamically adjust to changes in the model landscape. Furthermore, implementing a feedback loop mechanism that continuously monitors the performance of the ensemble and adjusts the weights based on real-time feedback can enhance the adaptability of PackLLM. By incorporating mechanisms for model evaluation, selection, and integration, PackLLM can effectively handle dynamic changes in the available LLMs during inference, ensuring optimal performance in evolving environments.