Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models

• URL: http://arxiv.org/abs/2406.09288v1
• Date: Thu, 13 Jun 2024 16:26:37 GMT
• Title: Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models
• Authors: Jinbin Zhang, Nasib Ullah, Rohit Babbar,
• Abstract summary: Extreme Zero-shot XMC (EZ-XMC) is a special setting of XMC wherein no supervision is provided. Traditional state-of-the-art methods extract pseudo labels from the document title or segments. We propose a framework to train a small bi-encoder model via the feedback from the large language model (LLM)
• Score: 3.908992369351976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extreme Multi-label Learning (XMC) is a task that allocates the most relevant labels for an instance from a predefined label set. Extreme Zero-shot XMC (EZ-XMC) is a special setting of XMC wherein no supervision is provided; only the instances (raw text of the document) and the predetermined label set are given. The scenario is designed to address cold-start problems in categorization and recommendation. Traditional state-of-the-art methods extract pseudo labels from the document title or segments. These labels from the document are used to train a zero-shot bi-encoder model. The main issue with these generated labels is their misalignment with the tagging task. In this work, we propose a framework to train a small bi-encoder model via the feedback from the large language model (LLM), the bi-encoder model encodes the document and labels into embeddings for retrieval. Our approach leverages the zero-shot ability of LLM to assess the correlation between labels and the document instead of using the low-quality labels extracted from the document itself. Our method also guarantees fast inference without the involvement of LLM. The performance of our approach outperforms the SOTA methods on various datasets while retaining a similar training time for large datasets.

Related papers

• Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss [6.244642999033755]
  Extreme Multi-label Classification (XMC) methods predict relevant labels for a given query in an extremely large label space. Recent works in XMC address this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. We propose PRIME, a XMC method that employs a novel prototypical contrastive learning technique to reconcile efficiency and performance surpassing brute-force approaches.
  arXiv  Detail & Related papers  (2024-10-27T10:24:23Z)
• Learning label-label correlations in Extreme Multi-label Classification via Label Features [44.00852282861121]
  Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Short-text XMC with label features has found numerous applications in areas such as query-to-ad-phrase matching in search ads, title-based product recommendation, prediction of related searches. We propose Gandalf, a novel approach which makes use of a label co-occurrence graph to leverage label features as additional data points to supplement the training distribution.
  arXiv  Detail & Related papers  (2024-05-03T21:18:43Z)
• Ground Truth Inference for Weakly Supervised Entity Matching [76.6732856489872]
  We propose a simple but powerful labeling model for weak supervision tasks. We then tailor the labeling model specifically to the task of entity matching. We show that our labeling model results in a 9% higher F1 score on average than the best existing method.
  arXiv  Detail & Related papers  (2022-11-13T17:57:07Z)
• LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [62.49198183539889]
  We propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning. Our proposed method is even highly competitive compared to the fully supervised counterpart with 100% labels.
  arXiv  Detail & Related papers  (2022-10-14T19:13:36Z)
• Large Loss Matters in Weakly Supervised Multi-Label Classification [50.262533546999045]
  We first regard unobserved labels as negative labels, casting the W task into noisy multi-label classification. We propose novel methods for W which reject or correct the large loss samples to prevent model from memorizing the noisy label. Our methodology actually works well, validating that treating large loss properly matters in a weakly supervised multi-label classification.
  arXiv  Detail & Related papers  (2022-06-08T08:30:24Z)
• Open Vocabulary Extreme Classification Using Generative Models [24.17018785195843]
  The extreme multi-label classification (XMC) task aims at tagging content with a subset of labels from an extremely large label set. We propose GROOV, a fine-tuned seq2seq model for OXMC that generates the set of labels as a flat sequence and is trained using a novel loss independent of predicted label order. We show the efficacy of the approach, experimenting with popular XMC datasets for which GROOV is able to predict meaningful labels outside the given vocabulary while performing on par with state-of-the-art solutions for known labels.
  arXiv  Detail & Related papers  (2022-05-12T00:33:49Z)
• Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification [27.33039900612395]
  We propose a novel metadata-induced contrastive learning (MICoL) method for large-scale multi-label text classification. MICoL exploits document metadata, which are widely available on the Web, to derive similar document-document pairs. We show that MICoL significantly outperforms strong zero-shot text classification and contrastive learning baselines.
  arXiv  Detail & Related papers  (2022-02-11T23:22:17Z)
• Extreme Zero-Shot Learning for Extreme Text Classification [80.95271050744624]
  Extreme Zero-Shot XMC (EZ-XMC) and Few-Shot XMC (FS-XMC) are investigated. We propose to pre-train Transformer-based encoders with self-supervised contrastive losses. We develop a pre-training method MACLR, which thoroughly leverages the raw text with techniques including Multi-scale Adaptive Clustering, Label Regularization, and self-training with pseudo positive pairs.
  arXiv  Detail & Related papers  (2021-12-16T06:06:42Z)
• Label Disentanglement in Partition-based Extreme Multilabel Classification [111.25321342479491]
  We show that the label assignment problem in partition-based XMC can be formulated as an optimization problem. We show that our method can successfully disentangle multi-modal labels, leading to state-of-the-art (SOTA) results on four XMC benchmarks.
  arXiv  Detail & Related papers  (2021-06-24T03:24:18Z)
• MATCH: Metadata-Aware Text Classification in A Large Hierarchy [60.59183151617578]
  MATCH is an end-to-end framework that leverages both metadata and hierarchy information. We propose different ways to regularize the parameters and output probability of each child label by its parents. Experiments on two massive text datasets with large-scale label hierarchies demonstrate the effectiveness of MATCH.
  arXiv  Detail & Related papers  (2021-02-15T05:23:08Z)
• Label-Wise Document Pre-Training for Multi-Label Text Classification [14.439051753832032]
  This paper develops Label-Wise Pre-Training (LW-PT) method to get a document representation with label-aware information. The basic idea is that, a multi-label document can be represented as a combination of multiple label-wise representations, and that, correlated labels always cooccur in the same or similar documents.
  arXiv  Detail & Related papers  (2020-08-15T10:34:27Z)

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.