Extreme Multi-Label Classification with Dual-Encoders
核心概念
Dual-encoder models can outperform SOTA methods in extreme multi-label classification tasks by using decoupled softmax loss and soft top-k operator-based loss.
摘要
The content discusses the use of dual-encoder models for extreme multi-label classification tasks. It highlights the limitations of existing contrastive losses and proposes new loss functions to improve performance. The study includes experiments on synthetic datasets and large benchmarks, showcasing the effectiveness of the proposed approach.
Directory:
Abstract
DE models are effective in retrieval tasks but underexplored in XMC.
Proposed decoupled softmax loss and soft top-k operator-based loss.
Introduction
DE models for openQA systems.
XMC scenarios require memorization and generalization.
Background: Multi-Label Classification
Definition of query-document relevance distribution.
Description of DE models and classification networks.
Improved Training of Dual-Encoder Models
Limitations of standard contrastive losses for XMC problems.
Proposal of DecoupledSoftmax loss and SoftTop-k operator-based loss.
Experiments
Comparison with existing XMC methods on various datasets.
Conclusions & Limitations
Dual-Encoders for Extreme Multi-Label Classification
統計資料
Current empirical evidence indicates that DE models fall significantly short on XMC benchmarks, where SOTA methods linearly scale the number of learnable parameters with the total number of classes (documents in the corpus) by employing per-class classification head.
When trained with proposed loss functions, standard DE models alone can match or outperform SOTA methods by up to 2% at Precision@1 even on the largest XMC datasets while being 20× smaller in terms of trainable parameters.
引述
"Our work shows that pure DE models can indeed match or even outperform SOTA XMC methods by up to 2% even on the largest public XMC benchmarks while being 20× smaller in model size."
How might advancements in dual-encoder models impact other areas of machine learning research
デュアルエンコーダーモデルの進歩は他の機械学習分野へどう影響しますか?
デュアルエンコーダーモデル技術は他の多くの機械学習分野へ革新的な影響を与える可能性があります。例えば自然言語処理(NLP)では文書生成や質問応答システム向上へ利用される見込みです。画像処理領域能でも物体認識精度向上や異常値検知等で活用される見通しがあります。
Dual-Encoders for Extreme Multi-Label Classification
Published as a conference paper at ICLR 2024
DUAL-ENCODERS FOR EXTREME MULTI-LABEL
CLASSIFICATION
Nilesh Gupta†⋄∗ Devvrit Khatri†⋄
Ankit Singh Rawat‡
Srinadh Bhojanapalli‡
Prateek Jain‡
Inderjit Dhillon†⋄
The University of Texas at Austin ⋄Google ‡Google Research ABSTRACT Dual-encoder (DE) models are widely used in retrieval tasks, most commonly stud-
ied on open QA benchmarks that are often characterized by multi-class and limited training data. In contrast, their performance in multi-label and data-rich retrieval settings like extreme multi-label classification (XMC), remains under-explored. Current empirical evidence indicates that DE models fall significantly short on XMC benchmarks, where SOTA methods (Dahiya et al., 2023a;b) linearly scale the number of learnable parameters with the total number of classes (documents in the corpus) by employing per-class classification head. To this end, we first study and highlight that existing multi-label contrastive training losses are not appropriate for training DE models on XMC tasks. We propose decoupled softmax loss – a simple modification to the InfoNCE loss – that overcomes the limitations of existing contrastive losses. We further extend our loss design to a soft top-k operator-based loss which is tailored to optimize top-k prediction performance. When trained with our proposed loss functions, standard DE models alone can match or outperform SOTA methods by up to 2% at Precision@1 even on the largest XMC datasets while being 20× smaller in terms of...
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Questions:
How can proposed decoupled softmax loss be applied other machine learning tasks beyond extreme multi label classification?
What potential challenges could arise when implementing these new lost function real world applications?
How might advancements dual encoder model impact other areas machine learning research?
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目錄
Extreme Multi-Label Classification with Dual-Encoders
Dual-Encoders for Extreme Multi-Label Classification
How can the proposed decoupled softmax loss be applied to other machine learning tasks beyond extreme multi-label classification
What potential challenges could arise when implementing these new loss functions in real-world applications
How might advancements in dual-encoder models impact other areas of machine learning research