Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?

TL;DR

This paper systematically compares pooling and attention strategies for LLM-based embedding models, revealing that no single design is best for all tasks and introducing a new multi-layer trainable pooling method that improves performance in certain tasks.

Contribution

It provides a large-scale, controlled comparison of pooling and attention strategies for LLM embeddings and proposes a novel multi-layer trainable pooling approach.

Findings

Bidirectional attention and trainable pooling outperform in similarity and retrieval tasks.

Simple pooling methods like EOS-last token are competitive in clustering and classification.

Multi-Layers Trainable Pooling significantly improves performance in text similarity and retrieval.

Abstract

The significant advancements of Large Language Models (LLMs) in generative tasks have led to a growing body of work exploring LLM-based embedding models. While these models, employing different pooling and attention strategies, have achieved state-of-the-art performance on public embedding benchmarks, questions still arise about what constitutes an effective design for LLM-based embedding models. However, these models are often trained on different datasets, using different LLM base models or training settings. Moreover, evaluations on public embedding benchmarks often fail to report statistical significance, making it difficult to determine which designs truly contribute to final performance. This complicates the process for practitioners seeking optimal training recipes for LLM-based embedding models. In this study, we conduct a large-scale experiment by training a series of LLM-based embedding models using the same training data and base model but differing in their pooling and attention strategies. The results show that there is no one-size-fits-all solution: while bidirectional attention and an additional trainable pooling layer outperform in text similarity and information retrieval tasks, they do not significantly surpass simpler designs like EOS-last token pooling and default causal attention in clustering and classification tasks. Furthermore, we propose a new pooling strategy, Multi-Layers Trainable Pooling, which transforms the outputs of all hidden layers, rather than just the last layer, using a cross-attention network. This method proves to be statistically superior in text similarity and retrieval tasks compared to existing pooling methods. Overall, this paper sheds light on effective training strategies for LLM-based embedding models.