Distilling Knowledge from Large Language Models to Empower Lightweight Sequential Recommenders

Alapfogalmak

Leveraging knowledge distillation, this work empowers lightweight conventional sequential recommendation models to match or even surpass the performance of complex large language model-based recommenders, while maintaining low inference latency.

Kivonat

This work investigates the challenge of efficiently leveraging the powerful semantic reasoning capabilities of Large Language Models (LLMs) in recommendation systems. LLM-based recommenders have demonstrated impressive performance, but suffer from high inference latency, limiting their practical deployment.
To address this issue, the authors propose a novel knowledge distillation strategy called DLLM2Rec, which distills knowledge from cumbersome LLM-based recommendation models to lightweight conventional sequential models. DLLM2Rec comprises two key components:

Importance-aware ranking distillation: This module filters reliable and student-friendly knowledge by weighting instances based on teacher confidence, ranking positions, and model consistency between the teacher and student.

Collaborative embedding distillation: This component bridges the semantic gap between the teacher and student models by projecting the teacher's embeddings to the student's embedding space and integrating them with collaborative signals mined from the data.

Extensive experiments on three real-world datasets demonstrate the effectiveness of DLLM2Rec. It boosts the performance of three typical sequential models by an average of 47.97%, and even enables them to surpass the LLM-based recommender in some cases, while maintaining low inference latency.

Statisztikák

The LLM-based recommender BIGRec requires 23,000 seconds (over 6 hours) to perform a single inference on the Amazon Games dataset, while the empowered student model by DLLM2Rec only takes 1.8 seconds.
On the MovieLens dataset, DLLM2Rec improves the HR@20 and NDCG@20 of the student model by 96.49% and 18.18% respectively, surpassing the LLM-based BIGRec.
On the Amazon Toys dataset, DLLM2Rec boosts the HR@20 and NDCG@20 of the student model by 100.43% and 56.35% respectively.

Idézetek

"Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance."
"However, the high inference latency of LLMs significantly restricts their practical deployment."
"Extensive experiments demonstrate the effectiveness of the proposed DLLM2Rec, boosting three typical sequential models with an average improvement of 47.97%, even enabling them to surpass LLM-based recommenders in some cases."

Főbb Kivonatok

Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model

by Yu Cui,Feng ... : arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00338.pdf

Mélyebb kérdések

How can the proposed DLLM2Rec framework be extended to other domains beyond recommendation systems, where knowledge distillation from large models to smaller models is desirable

The DLLM2Rec framework can be extended to other domains beyond recommendation systems where knowledge distillation from large models to smaller models is desirable by adapting the key components of the framework to suit the specific characteristics of the new domain. Here are some ways in which the framework can be extended:

Natural Language Processing (NLP): In NLP tasks such as text generation or sentiment analysis, large language models like GPT-3 can be distilled into smaller models for faster inference. The importance-aware ranking distillation can be used to filter reliable knowledge, while collaborative embedding distillation can help align the semantic spaces between the teacher and student models.

Computer Vision: In image recognition tasks, large convolutional neural networks (CNNs) can be distilled into smaller models for deployment on edge devices. The ranking distillation can be adapted to prioritize important features in the image, while collaborative embedding distillation can help transfer knowledge about image features.

Healthcare: In healthcare applications like medical image analysis or patient diagnosis, large models trained on extensive datasets can be distilled into smaller models for real-time decision-making. The importance-aware ranking distillation can focus on critical patient cases, while collaborative embedding distillation can help integrate medical knowledge.

Finance: In financial forecasting or fraud detection, large models can be distilled into smaller models for quicker analysis of market trends or risk assessment. The ranking distillation can prioritize important financial indicators, while collaborative embedding distillation can incorporate domain-specific knowledge.

By customizing the distillation strategies and components of DLLM2Rec to the specific requirements and data characteristics of different domains, the framework can effectively transfer knowledge from large models to smaller models in various applications.

What are the potential limitations of the importance-aware ranking distillation and collaborative embedding distillation approaches, and how could they be further improved

The importance-aware ranking distillation and collaborative embedding distillation approaches in DLLM2Rec have certain limitations that could be further improved:

Importance-aware Ranking Distillation Limitations:

Reliability of Importance Weights: The reliability of the confidence-aware and consistency-aware weights may vary based on the quality of the teacher's recommendations. Improving the robustness of these weights through additional validation mechanisms could enhance the distillation process.
Scalability: Handling a large number of instances and ensuring the scalability of importance-aware ranking distillation could be a challenge. Developing efficient algorithms or parallel processing techniques could address this limitation.

Collaborative Embedding Distillation Limitations:

Semantic Gap: Despite the offset term, aligning embeddings from different semantic spaces may still pose challenges. Exploring more advanced techniques like adversarial training or domain adaptation could help bridge the semantic gap more effectively.
Model Complexity: The complexity of the collaborative embedding distillation process could impact the overall efficiency of the framework. Simplifying the integration of teacher and student embeddings while preserving essential information could improve the approach.

To enhance these approaches, further research could focus on refining the weighting mechanisms, optimizing the collaborative embedding mapping, and exploring novel strategies to address the identified limitations.

Given the significant performance gap between LLM-based and conventional recommendation models, what other techniques beyond knowledge distillation could be explored to bridge this gap while maintaining low inference latency

Beyond knowledge distillation, several techniques can be explored to bridge the performance gap between LLM-based and conventional recommendation models while maintaining low inference latency:

Hybrid Models: Combining the strengths of LLM-based models in semantic reasoning with the efficiency of conventional models in collaborative filtering can lead to hybrid recommendation systems. These models can leverage the complementary aspects of both approaches to improve overall performance.

Transfer Learning: Utilizing transfer learning techniques to fine-tune pre-trained LLMs on recommendation-specific tasks can enhance the model's performance. By transferring knowledge from a pre-trained LLM to a smaller model, the gap in performance can be reduced.

Ensemble Methods: Ensemble methods, such as model stacking or boosting, can be employed to combine predictions from multiple models, including LLM-based and conventional models. This ensemble approach can leverage the diversity of models to improve recommendation accuracy.

Active Learning: Incorporating active learning strategies to selectively query instances that are most informative for model improvement can enhance the performance of recommendation systems. By focusing on high-impact data points, the models can learn more efficiently.

Graph Neural Networks (GNNs): Integrating GNNs into recommendation systems can capture complex relationships in user-item interactions and enhance the model's ability to make accurate recommendations. GNNs can complement the semantic reasoning of LLMs with graph-based collaborative filtering.

By exploring these techniques in conjunction with knowledge distillation, recommendation systems can bridge the performance gap between LLM-based and conventional models while ensuring low inference latency.