Leveraging Modular Pre-trained Graphformer for Efficient Web-scale Learning to Rank

Konsep Inti

The proposed MPGraf model leverages a modular and pre-trained graphformer architecture to cohesively integrate the regression capabilities of Transformers with the link prediction strengths of Graph Neural Networks (GNNs) for efficient web-scale learning to rank.

Abstrak

The paper introduces MPGraf, a novel model for web-scale learning to rank (LTR) that combines the strengths of Transformers and Graph Neural Networks (GNNs).

The key highlights are:

1. Graph Construction with Link Rippling:

   • MPGraf generates high-quality pseudo-label links for unlabeled query-webpage pairs using a self-tuning approach.
   • It conducts Query-centered Expanding Ripple and Webpage-centered Shrinking Ripple to construct the query-webpage bipartite graphs.

2. Representation Learning with Hybrid Graphformer:

   • MPGraf leverages a hybrid graphformer architecture that consists of a GNN module and a Transformer module.
   • The graphformer can be designed in either a stacking or parallelizing manner to extract generalizable representations.

3. Surgical Fine-tuning with Modular Composition:

   • MPGraf pre-trains the GNN, Transformer, and MLP modules on large-scale LTR datasets.
   • It then employs a surgical fine-tuning strategy, where certain module parameters are frozen while others are fine-tuned on the target dataset, to overcome distribution shifts.

The extensive offline and online experiments demonstrate the superior performance of MPGraf compared to state-of-the-art LTR models, especially in web-scale search scenarios.

Statistik

MPGraf achieves 1.64%, 1.65%, 1.43% and 1.74% improvements in NDCG@10 over MLP with NeuralNDCG on the commercial dataset under different ratios of labeled data.
MPGraf outperforms the legacy online system with 0.36% and 0.45% improvements on ∆AB, and 3.34% and 6.67% improvements on ∆GSB for random and long-tail queries, respectively.

Kutipan

"MPGraf could learn better generalizable representations with the graphformer architecture for downstream ranking tasks compared with baselines."
"Contrary to the conventional fine-tuning strategy of directly fine-tuning the whole model, freezing certain layer parameters can be advantageous since, based on the interplay between the pre-training and target datasets, some parameters in these modules, which have been trained on the pre-training dataset, may already approximate a minimum for the target distribution."

Wawasan Utama Disaring Dari

Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)

by Yuchen Li, H... pada arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16590.pdf

Pertanyaan yang Lebih Dalam

How can the proposed modular and pre-trained graphformer architecture be extended to other domains beyond web-scale learning to rank, such as recommender systems or knowledge graph reasoning?

The modular and pre-trained graphformer architecture, as proposed in MPGraf, can be effectively extended to other domains such as recommender systems and knowledge graph reasoning by leveraging its inherent flexibility and capacity for representation learning.

Recommender Systems: In recommender systems, the architecture can be adapted to model user-item interactions as bipartite graphs, similar to the query-webpage pairs in LTR. By utilizing the graphformer’s ability to capture both local and global relationships through GNNs and Transformers, the model can learn user preferences and item characteristics more effectively. The modular design allows for the integration of additional features, such as user demographics or item metadata, enhancing the model's ability to provide personalized recommendations. Furthermore, the surgical fine-tuning strategy can be employed to adapt the model to specific user behavior patterns, addressing the distribution shifts often encountered in dynamic recommendation environments.

Knowledge Graph Reasoning: For knowledge graph reasoning, the graphformer can be utilized to enhance the understanding of entity relationships and attributes. By constructing knowledge graphs where entities are nodes and relationships are edges, the architecture can facilitate reasoning tasks such as link prediction and entity classification. The pre-training phase can involve large-scale knowledge graph datasets, allowing the model to learn rich representations of entities and their interconnections. The hybrid approach of stacking and parallelizing GNN and Transformer modules can be particularly beneficial in capturing complex relational patterns, making it suitable for tasks like question answering and semantic search within knowledge graphs.

Cross-Domain Adaptability: The modular nature of the architecture allows for easy adaptation to various domains by swapping out specific components or adjusting the training objectives. For instance, in a recommender system, the loss functions can be tailored to optimize for metrics like Mean Average Precision (MAP) or Hit Rate, while in knowledge graph reasoning, the focus could shift to optimizing for accuracy in entity predictions or relationship inferences.

What are the potential limitations of the surgical fine-tuning strategy, and how can it be further improved to better handle distribution shifts across a wider range of datasets?

The surgical fine-tuning strategy employed in MPGraf presents several potential limitations, particularly in its ability to generalize across diverse datasets and mitigate distribution shifts effectively.

Limited Generalization: While freezing certain layers during fine-tuning can help retain previously learned knowledge, it may also restrict the model's ability to adapt to new data distributions. If the frozen parameters are not well-aligned with the target dataset, the model may underperform. This limitation can be particularly pronounced in scenarios where the target dataset exhibits significant differences in feature distributions compared to the pre-training datasets.

Overfitting Risks: The surgical fine-tuning approach may lead to overfitting, especially when the target dataset is small or lacks diversity. The model might become too specialized in the nuances of the target data, losing its generalization capabilities.

Improvement Strategies: To enhance the surgical fine-tuning strategy, several approaches can be considered:

Dynamic Layer Freezing: Instead of a static freezing strategy, a dynamic approach could be implemented where layers are selectively unfrozen based on their performance on validation data. This would allow the model to adaptively adjust its parameters in response to the target dataset's characteristics.
Multi-Task Learning: Incorporating multi-task learning during the fine-tuning phase can help the model learn shared representations across related tasks, improving its robustness to distribution shifts. By training on auxiliary tasks that are relevant to the target domain, the model can develop a more generalized understanding.
Domain Adaptation Techniques: Implementing domain adaptation techniques, such as adversarial training or domain-invariant feature learning, can help the model better handle distribution shifts. These techniques can encourage the model to learn representations that are less sensitive to the specific characteristics of the training data.

Given the success of large language models in various NLP tasks, how can MPGraf leverage such models to enhance the representation learning capabilities for web-scale search?

MPGraf can leverage large language models (LLMs) to significantly enhance its representation learning capabilities for web-scale search in several ways:

Pre-trained Language Representations: By integrating LLMs, MPGraf can utilize pre-trained embeddings that capture rich semantic information from vast text corpora. These embeddings can serve as initial representations for queries and webpages, providing a strong foundation for further learning. The contextual understanding embedded in LLMs can improve the model's ability to discern nuanced meanings and relationships in search queries.

Fine-tuning with LLMs: The architecture can be adapted to fine-tune LLMs specifically for the search domain. By training the LLMs on domain-specific datasets, MPGraf can enhance the model's understanding of search intent and relevance, leading to improved ranking performance. This fine-tuning can be performed in conjunction with the surgical fine-tuning strategy, allowing for a more comprehensive adaptation to the target dataset.

Hybrid Model Architecture: MPGraf can adopt a hybrid architecture that combines LLMs with its existing GNN and Transformer modules. For instance, the LLM can be used to process textual features of queries and webpages, while the GNN can model the relationships between them. This integration allows for a more holistic representation that captures both textual semantics and structural relationships, enhancing the overall performance of the ranking system.

Query Expansion and Reformulation: LLMs can assist in query expansion and reformulation by generating alternative queries that capture the same intent. This capability can be integrated into MPGraf to improve the diversity of queries considered during the ranking process, ultimately leading to better retrieval performance.

Contextual Understanding: The ability of LLMs to understand context can be harnessed to improve the relevance of search results. By incorporating contextual embeddings into the ranking process, MPGraf can better align search results with user intent, leading to higher user satisfaction and engagement.

By leveraging the strengths of large language models, MPGraf can enhance its representation learning capabilities, making it more effective in addressing the complexities of web-scale search tasks.