Out-of-the-Box Conditional Text Embeddings from Large Language Models (2504.16411v1)

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 LLM 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.

Unsupervised Conditional Text Embeddings from LLMs

The paper "Out-of-the-Box Conditional Text Embeddings from LLMs" introduces PonTE, an innovative approach for creating conditional text embeddings without the need for labor-intensive model fine-tuning. Traditional text embeddings generate universal representations for sentences, which can mask variations in meaning when conditioned on different aspects. PonTE circumvents these limitations by leveraging causal LLMs and carefully crafted prompts to produce embeddings that reflect specific conditions.

Methodology

PonTE is distinguished by its use of causal LLMs, such as Mistral and Llama models, which are employed with conditional prompting to produce rich semantic vectors sensitive to specified conditions. Key to PonTE is its prompt design, which solicits embeddings by asking LLMs to condense a given text into a single word under a specific condition (e.g., "Express this text 'A' in one word in terms of 'B'"). This technique induces the model to focus its semantic representation on the specific aspect denoted by 'B', ensuring that the embedding reflects the desired conditional context.

Experimental Results

The capabilities of PonTE were validated through tasks in conditional semantic text similarity (C-STS) and text clustering. On C-STS, PonTE demonstrated remarkable performance, often on par with leading supervised methods, despite operating in an unsupervised framework. Notably, PonTE using Llama-3-8B-Inst reached Spearman and Pearson correlation coefficients of 37.1 and 33.6, respectively. These results underscore PonTE's efficacy at generating embeddings that capture nuanced, condition-specific semantics without training on C-STS-specific data.

In text clustering tasks, PonTE further showcased its versatility. When applied to datasets such as the Amazon reviews corpus and ScienceQA, it yielded competitive V-measure scores. PonTE excelled in clustering tasks that demand sensitivity to topic distinctions as well as emotional tones, highlighting its multi-faceted application potential.

Analysis and Implications

PonTE's results suggest that unsupervised methods can rival traditional supervised approaches in generating conditional text embeddings. This is particularly significant in domains where annotated data is sparse or unavailable. The reliance on LLMs establishes a foundation for applications across varied languages and domains, provided the availability of robust prompting techniques. With the increasing scale and power of LLMs, PonTE anticipates a future where such models are fine-tuned less often, dramatically reducing resource and time investments while maintaining high performance across diverse NLP tasks.

Conclusion and Future Directions

The introduction of PonTE represents a promising advance in the field of text embeddings, enabling the generation of condition-specific semantic vectors without extensive training data. The results on C-STS and clustering tasks indicate that PonTE could serve as an important tool for NLP practitioners and researchers exploring text representation from multiple perspectives. Future research could focus on refining prompt engineering and extending PonTE's methodology to include a broader spectrum of tasks and languages, potentially expanding the impact of LLMs in new and unforeseen ways.