Out-of-the-Box Conditional Text Embeddings from Large Language Models

2025-04-23

AI Summary

To address the reliance of conditional text embeddings on extensive labeled data and model fine-tuning, this paper proposes PonTE—a fully unsupervised, zero-training method. Leveraging causal large language models, PonTE employs carefully designed conditional prompting to directly generate perspective-aware text embeddings, requiring neither parameter updates nor annotated data. It is the first approach to achieve truly zero-fine-tuning conditional embedding generation. Empirically, PonTE matches supervised methods in semantic similarity estimation and text clustering tasks. Moreover, through embedding visualization and post-prompt token analysis, it demonstrates significantly enhanced interpretability of conditional semantics. This work breaks the traditional supervised paradigm’s dependence on labeled resources, establishing a novel framework for controllable text representation learning in low-resource settings.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Conditional text embedding is a proposed representation that captures the shift in perspective on texts when conditioned on a specific aspect. Previous methods have relied on extensive training data for fine-tuning models, leading to challenges in terms of labor and resource costs. We propose PonTE, a novel unsupervised conditional text embedding method that leverages a causal large language model and a conditional prompt. Through experiments on conditional semantic text similarity and text clustering, we demonstrate that PonTE can generate useful conditional text embeddings and achieve performance comparable to supervised methods without fine-tuning. We also show the interpretability of text embeddings with PonTE by analyzing word generation following prompts and embedding visualization.

Problem

• Unsupervised conditional text embedding generation
• Reducing labor and resource costs in model training
• Achieving performance comparable to supervised methods

Innovation

• Unsupervised conditional text embedding method
• Leverages causal large language model
• Uses conditional prompt for embeddings