A General Framework for Producing Interpretable Semantic Text Embeddings (2410.03435v1)

Abstract: Semantic text embedding is essential to many tasks in NLP. While black-box models are capable of generating high-quality embeddings, their lack of interpretability limits their use in tasks that demand transparency. Recent approaches have improved interpretability by leveraging domain-expert-crafted or LLM-generated questions, but these methods rely heavily on expert input or well-prompt design, which restricts their generalizability and ability to generate discriminative questions across a wide range of tasks. To address these challenges, we introduce \algo{CQG-MBQA} (Contrastive Question Generation - Multi-task Binary Question Answering), a general framework for producing interpretable semantic text embeddings across diverse tasks. Our framework systematically generates highly discriminative, low cognitive load yes/no questions through the \algo{CQG} method and answers them efficiently with the \algo{MBQA} model, resulting in interpretable embeddings in a cost-effective manner. We validate the effectiveness and interpretability of \algo{CQG-MBQA} through extensive experiments and ablation studies, demonstrating that it delivers embedding quality comparable to many advanced black-box models while maintaining inherently interpretability. Additionally, \algo{CQG-MBQA} outperforms other interpretable text embedding methods across various downstream tasks.

• The paper presents the CQG-MBQA framework that enhances semantic text embeddings by generating human-understandable binary questions.
• The paper employs contrastive learning to derive discriminative questions, balancing high embedding quality with improved interpretability.
• The paper validates the framework through standard benchmarks, showing scalable performance and reduced cognitive load in real-world NLP tasks.

Overview of "A General Framework for Producing Interpretable Semantic Text Embeddings"

This paper addresses the critical challenge of interpretability in semantic text embeddings, a foundational aspect of modern NLP. While high-quality embeddings have been successfully generated by black-box models such as Sentence-BERT, SimCSE, and others, their opacity regarding interpretability remains a concern, particularly in domains requiring transparency like legal or medical applications. The authors introduce a novel framework, CQG-MBQA (Contrastive Question Generation - Multi-task Binary Question Answering), which provides interpretable text embeddings across diverse tasks.

Key Contributions

1. Innovative Framework Design:
   • The CQG-MBQA framework incorporates the generation of binary yes/no questions to construct interpretable embedding dimensions efficiently. This contrasts with previous methods that require expert-crafted or heavily-prompted inputs, enabling better generalizability and consistency.
2. Contrastive Question Generation (CQG):
   • Utilizing contrastive learning principles, the CQG method generates discriminative questions that form the basis of the embedding space, allowing for human-understandable insights into semantic nuances. The framework strategically contrasts positive, hard negative, and easy negative samples to derive these questions.
3. Multi-task Binary Question Answering (MBQA):
   • This model addresses the scalability issues by efficiently processing generated questions, reducing inference costs typical of LLMs. MBQA reproduces LLM-generated answers with impressive accuracy, proving both cost-effective and scalable.
4. Experimental Validation:
   • Through extensive experiments on standard benchmarks like Semantic Textual Similarity (STS), retrieval tasks, and clustering, CQG-MBQA was shown to deliver embedding quality on par with advanced models while vastly surpassing existing interpretable methods.

Numerical Results and Observations

The framework's effectiveness is underscored by numerical results demonstrating competitive performance in embedding quality while significantly enhancing interpretability. For instance, the cognitive load associated with CQG-MBQA embeddings is notably lower compared to QAEmb-MBQA, underscoring the improved interpretability without degrading semantic capture.

Implications and Future Directions

The research presents a scalable and cost-effective method for generating interpretable text embeddings with applications in high-stakes decision-making environments. The future trajectory of this research could involve refining the CQG-MBQA model to further balance the trade-off between interpretability and embedding quality. Additionally, integrating this framework with different types of downstream tasks and exploring its adaptability to emerging LLMs could expand its applicability and robustness.

Conclusion

The CQG-MBQA framework presents a promising approach to interpretable text embeddings, effectively addressing challenges of opacity without compromising on quality. Its ability to deliver insights into semantic relationships while reducing computational costs positions it as a valuable tool in both academia and industry, particularly in fields requiring high transparency. The integration of contrastive learning principles with a flexible question-answering model is an instructive step forward in the evolution of NLP methodologies, offering both practical and theoretical advancements.