SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines (2401.03038v2)

Abstract: LLMs are being increasingly deployed as part of pipelines that repeatedly process or generate data of some sort. However, a common barrier to deployment are the frequent and often unpredictable errors that plague LLMs. Acknowledging the inevitability of these errors, we propose {\em data quality assertions} to identify when LLMs may be making mistakes. We present SPADE, a method for automatically synthesizing data quality assertions that identify bad LLM outputs. We make the observation that developers often identify data quality issues during prototyping prior to deployment, and attempt to address them by adding instructions to the LLM prompt over time. SPADE therefore analyzes histories of prompt versions over time to create candidate assertion functions and then selects a minimal set that fulfills both coverage and accuracy requirements. In testing across nine different real-world LLM pipelines, SPADE efficiently reduces the number of assertions by 14\% and decreases false failures by 21\% when compared to simpler baselines. SPADE has been deployed as an offering within LangSmith, LangChain's LLM pipeline hub, and has been used to generate data quality assertions for over 2000 pipelines across a spectrum of industries.

• The paper proposes a two-component framework using prompt deltas and ILP-based filtering to automatically generate and select assertions for LLM pipelines.
• It demonstrates a 14% reduction in assertion counts and a 21% decrease in false failure rates across nine diverse LLM pipelines.
• The system is validated in real-world deployments with over 1300 runs, providing a practical advance in enhancing LLM pipeline reliability.

Synthesizing Assertions for LLM Pipelines: An Evaluation of "spade"

The paper "spade: Synthesizing Assertions for LLM Pipelines" addresses the challenges inherent in deploying LLMs within data-generation pipelines. Given the propensity for errors and unpredictable outputs from LLMs, the authors propose a systematic method to automatically generate assertions aimed at identifying incorrect LLM outputs within these pipelines.

Overview of Methodology

This work is structured around a two-component framework, spade, designed to enhance the robustness of LLM data pipelines. The core contributions include:

1. Assertion Generation from Prompt Deltas: The authors introduce prompt deltas, defined as differences between consecutive versions of a prompt template, as a rich source of assertion criteria. A taxonomy is developed to classify prompt deltas into categories such as Response Format Instruction, Workflow Description, and Qualitative Criteria. By leveraging nine real-world pipelines, the authors extract criteria from prompt deltas and utilize LLMs to generate candidate assertion functions.
2. Filtering Assertions via ILP and Subsumption: The candidate assertions, often redundant or inaccurate, necessitate filtering. The authors formalize an Integer Linear Program (ILP) to select a minimal set of assertions fulfilling coverage and accuracy requirements. Furthermore, they introduce a novel "subsumption" strategy, ensuring that selected assertions can collectively cover potential failures while adhering to predefined thresholds for false failure rates (FFR) and coverage.

Empirical Evaluation

The paper details an extensive evaluation over nine diverse LLM pipelines, including tasks from generating negotiation strategies to summarizing lectures. This evaluation demonstrates:

• Reduction in Assertion Count: spade efficiently reduces the number of assertions by 14% on average when leveraging subsumption, compared to a baseline that does not employ filtering comprehensively.
• Decrease in False Failure Rates: A reduction of 21% in false failure rates is achieved, underscoring the effectiveness of the filtering mechanism in maintaining assertion accuracy while avoiding unnecessary duplication.
• Practical Deployment and User Adoption: The prototype spade system, deployed via a web interface, garnered significant user engagement with over 1300 runs, highlighting its utility in real-world applications.

Theoretical Implications and Future Directions

The paper substantially contributes to the field by formulating the assertion selection problem as NP-hard and proposing solutions that balance between computational feasibility and coverage comprehensiveness. The use of LLMs both as a source of assertions and as evaluators in the pipeline reflects an innovative recursion of AI for AI, indicating potential for further automation in LLM deployment tasks.

Future developments might explore expanding spade's applicability to more complex, multi-step LLM pipelines, or enhancing robustness using advanced prompt engineering strategies. Integrating passive data collection methods for labeled examples and developing methodologies to continuously refine assertions in line with evolving LLM capabilities would be valuable extensions.

In summary, by proposing spade, the authors advance automated methods to improve reliability in LLM-driven tasks, providing an essential tool for researchers and practitioners aiming to utilize LLMs more confidently in production environments. This alignment of assertion synthesis with LLM pipeline dynamics illustrates a practical step forward in operationalizing AI amidst the inherent unpredictability of model outputs.