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

• URL: http://arxiv.org/abs/2409.02727v2
• Date: Thu, 5 Sep 2024 07:17:59 GMT
• Title: Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?
• Authors: Yixuan Tang, Yi Yang,
• Abstract summary: 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 paper sheds light on effective training strategies for LLM-based embedding models.
• Score: 18.990655668481075
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Comprehensive Analysis on LLM-based Node Classification Algorithms [21.120619437937382]
  We develop a comprehensive and testbed for node classification using Large Language Models (LLMs) It includes ten datasets, eight LLM-based algorithms, and three learning paradigms, and is designed for easy extension with new methods and datasets. We conduct extensive experiments, training and evaluating over 2,200 models, to determine the key settings that affect performance. Our findings uncover eight insights, e.g., LLM-based methods can significantly outperform traditional methods in a semi-supervised setting, while the advantage is marginal in a supervised setting.
  arXiv  Detail & Related papers  (2025-02-02T15:56:05Z)
• Towards Better Understanding Table Instruction Tuning: Decoupling the Effects from Data versus Models [62.47618742274461]
  We fine-tune base models from the Mistral, OLMo, and Phi families on existing public training datasets. Our replication achieves performance on par with or surpassing existing table LLMs. We decouple the contributions of training data and the base model, providing insight into their individual impacts.
  arXiv  Detail & Related papers  (2025-01-24T18:50:26Z)
• The Inherent Limits of Pretrained LLMs: The Unexpected Convergence of Instruction Tuning and In-Context Learning Capabilities [51.594836904623534]
  We investigate whether instruction-tuned models possess fundamentally different capabilities from base models that are prompted using in-context examples. We show that the performance of instruction-tuned models is significantly correlated with the in-context performance of their base counterparts. Specifically, we extend this understanding to instruction-tuned models, suggesting that their pretraining data similarly sets a limiting boundary on the tasks they can solve.
  arXiv  Detail & Related papers  (2025-01-15T10:57:55Z)
• LLMs are Also Effective Embedding Models: An In-depth Overview [40.53941563464671]
  Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs like GPT, LLaMA, and Mistral.
  arXiv  Detail & Related papers  (2024-12-17T06:48:24Z)
• Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective [0.0]
  Transformer-based models like BERT and GPT rely on pooling layers to aggregate token-level embeddings into sentence-level representations. Common pooling mechanisms such as Mean, Max, and Weighted Sum play a pivotal role in this aggregation process. This paper investigates the effects of these pooling mechanisms on two prominent LLM families -- BERT and GPT, in the context of sentence-level sentiment analysis.
  arXiv  Detail & Related papers  (2024-11-22T00:59:25Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild [84.57103623507082]
  This paper introduces Model-GLUE, a holistic Large Language Models scaling guideline. We benchmark existing scaling techniques, especially selective merging, and variants of mixture. We then formulate an optimal strategy for the selection and aggregation of a heterogeneous model zoo. Our methodology involves the clustering of mergeable models and optimal merging strategy selection, and the integration of clusters.
  arXiv  Detail & Related papers  (2024-10-07T15:55:55Z)
• SoupLM: Model Integration in Large Language and Multi-Modal Models [51.12227693121004]
  Training large language models (LLMs) requires significant computing resources. Existing publicly available LLMs are typically pre-trained on diverse, privately curated datasets spanning various tasks.
  arXiv  Detail & Related papers  (2024-07-11T05:38:15Z)
• NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models [38.41524186248607]
  We introduce the NV-Embed model, incorporating architectural designs, training procedures, and curated datasets. For model architecture, we propose a latent attention layer to obtain pooled embeddings, which consistently improves retrieval and downstream task accuracy. For training data, we utilize the hard-negative mining, synthetic data generation and existing public available datasets to boost the performance of embedding model.
  arXiv  Detail & Related papers  (2024-05-27T17:59:45Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.