Semantic Operators: A Declarative Model for Rich, AI-based Data Processing (2407.11418v3)

Abstract: The semantic capabilities of LLMs have the potential to enable rich analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems either empirically optimize expensive LLM-powered operations with no performance guarantees, or serve a limited set of row-wise LLM operations, providing limited robustness, expressiveness and usability. We introduce semantic operators, the first formalism for declarative and general-purpose AI-based transformations based on natural language specifications (e.g., filtering, sorting, joining or aggregating records using natural language criteria). Each operator opens a rich space for execution plans, similar to relational operators. Our model specifies the expected behavior of each operator with a high-quality gold algorithm, and we develop an optimization framework that reduces cost, while providing accuracy guarantees with respect to a gold algorithm. Using this approach, we propose several novel optimizations to accelerate semantic filtering, joining, group-by and top-k operations by up to $1,000\times$. We implement semantic operators in the LOTUS system and demonstrate LOTUS' effectiveness on real, bulk-semantic processing applications, including fact-checking, biomedical multi-label classification, search, and topic analysis. We show that the semantic operator model is expressive, capturing state-of-the-art AI pipelines in a few operator calls, and making it easy to express new pipelines that match or exceed quality of recent LLM-based analytic systems by up to $170\%$, while offering accuracy guarantees. Overall, LOTUS programs match or exceed the accuracy of state-of-the-art AI pipelines for each task while running up to $3.6\times$ faster than the highest-quality baselines. LOTUS is publicly available at https://github.com/lotus-data/lotus.

• The paper introduces semantic operators that expand traditional relational models with advanced AI functions for handling both structured and unstructured data.
• LOTUS’s declarative query model abstracts LLM intricacies, streamlining complex reasoning tasks through operator optimizations like batching and semantic indexing.
• Empirical evaluations demonstrate up to 9.5% higher fact-checking accuracy and 800 times faster extreme multi-label classification, highlighting its efficiency across applications.

Overview of "LOTUS: Enabling Semantic Queries with LLMs Over Tables of Unstructured and Structured Data"

The paper entitled "LOTUS: Enabling Semantic Queries with LLMs Over Tables of Unstructured and Structured Data" presents LOTUS, a system designed to bridge the gap between LLMs and traditional relational databases. This system facilitates declarative semantic queries that handle both structured and unstructured data. By extending the relational model with semantic operators, LOTUS enables complex reasoning tasks over vast datasets with relatively low development overhead.

The authors introduce several semantic operators that complement traditional relational operations. These operators include semantic filtering, joining, similarity joining, aggregation, ranking, mapping, extraction, clustering, and searching. By leveraging these operators, users can compose intricate AI-driven query pipelines that address diverse applications such as fact-checking, extreme multi-label classification, and search.

Key Contributions

1. Semantic Operators: The introduction of semantic operators forms the crux of the paper's contribution. These operators extend the conventional relational model with AI-based operations that are designed to handle semantic queries. The paper elaborates on the design and implementation of each operator, providing examples and use-cases.
2. Declarative Programming Model: LOTUS presents a declarative programming model that abstracts the complexity of working with LLMs. This model separates the user-specified logical query plan from its execution, akin to how SQL abstracts database implementations. The system automatically manages the low-level details such as model context length limits, making it easier for developers to leverage LLMs for complex reasoning tasks.
3. Efficiency and Optimization: The paper details several optimizations for the semantic operators. These include model cascades, batching, and leveraging semantic indices for efficient query processing. These optimizations are crucial in ensuring that the system executes queries efficiently while maintaining high result quality.

Implementation and Evaluation

The authors evaluate LOTUS across three wide-ranging applications: fact-checking, extreme multi-label classification, and search and ranking. Each of these applications demonstrates the versatility and effectiveness of LOTUS' programming model.

Fact-Checking:

On the FEVER dataset, LOTUS programs could reproduce and improve upon the results of FacTool, a recent state-of-the-art fact-checking pipeline. The LOTUS implementation achieved up to 9.5% higher accuracy and 7-34 times lower execution time. This was made possible through a combination of semantic filtering and joining.

Extreme Multi-Label Classification:

For the extreme multi-label classification task on the BioDEX dataset, LOTUS demonstrated state-of-the-art result quality while providing an efficient algorithm that runs up to 800 times faster than a naive join. This substantial efficiency gain showcases the effectiveness of LOTUS' map-search-filter join pattern.

Search and Ranking:

In search and ranking applications, such as on the SciFact and CIFAR-bench datasets, LOTUS' semantic top-k operator significantly outperformed baseline methods by 5.9-49.4% in terms of nDCG@10. This highlights the system's capability to support complex ranking criteria while being highly efficient.

Implications and Future Directions

Practical Implications:

LOTUS holds significant practical implications for researchers and data professionals who deal with large corpora of mixed data types. Its ability to seamlessly integrate structured and unstructured data into a unified query framework can streamline workflows and enable richer, more nuanced data analytics.

Theoretical Implications:

From a theoretical standpoint, the introduction of semantic operators enriches the relational algebra traditionally used in database systems. This could pave the way for further research into optimizing semantic queries and extending the model to support even more complex operations.

Future Developments:

Future developments in LOTUS could explore additional query optimizations, automatic prompt optimization techniques, and expanded support for various embedding stores and indices. Additionally, integrating learning-based methods for automatically estimating correlations and optimizing pipeline configurations could further enhance LOTUS' efficiency and scalability.

Conclusion

The paper presents LOTUS as a robust framework that not only extends but transforms traditional data querying paradigms by integrating advanced semantic capabilities of LLMs. Through a comprehensive set of semantic operators and a declarative programming interface, LOTUS enables efficient and expressive semantic queries over large datasets, making it a valuable tool for both practical applications and theoretical exploration in the field of AI and data management.