PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines (2504.14738v1)

Abstract: LLMs are increasingly deployed in specialized production data processing pipelines across diverse domains -- such as finance, marketing, and e-commerce. However, when running them in production across many inputs, they often fail to follow instructions or meet developer expectations. To improve reliability in these applications, creating assertions or guardrails for LLM outputs to run alongside the pipelines is essential. Yet, determining the right set of assertions that capture developer requirements for a task is challenging. In this paper, we introduce PROMPTEVALS, a dataset of 2087 LLM pipeline prompts with 12623 corresponding assertion criteria, sourced from developers using our open-source LLM pipeline tools. This dataset is 5x larger than previous collections. Using a hold-out test split of PROMPTEVALS as a benchmark, we evaluated closed- and open-source models in generating relevant assertions. Notably, our fine-tuned Mistral and Llama 3 models outperform GPT-4o by 20.93% on average, offering both reduced latency and improved performance. We believe our dataset can spur further research in LLM reliability, alignment, and prompt engineering.

• The paper introduces PromptEvals, a dataset of over 12,000 assertion criteria for more than 2,000 prompt templates, designed to build robust guardrails for custom production LLM pipelines.
• Benchmarking on PromptEvals revealed that fine-tuned open-source models like Mistral and Llama 3 significantly outperform GPT-4o in generating relevant assertion criteria, with fine-tuned Mistral showing a 20.93% greater semantic F1 score.
• PromptEvals provides resources for developers to create more reliable and performance-consistent LLM applications, highlighting the potential for using smaller, cost-effective, fine-tuned models in production settings.

An In-Depth Analysis of PromptEvals: A Dataset for LLM Pipeline Assertion Criteria

The paper, "PromptEvals: A Dataset of Assertions and Guardrails for Custom Production LLM Pipelines," introduces a substantial dataset designed to enhance the reliability of LLM outputs in specialized production pipelines. The proliferation of LLMs across diverse domains such as finance, marketing, and e-commerce necessitates robust frameworks to ensure models adhere to specific instructions and fulfill developer expectations. The authors present PromptEvals, a dataset compiled to facilitate the development and deployment of these frameworks through assertions or guardrails.

Dataset Composition and Methodology

PromptEvals consists of 2087 prompt templates accompanied by 12,623 assertion criteria developed by experienced developers utilizing open-source LLM pipeline tools. This dataset is notable for being five times larger than prior collections, providing extensive coverage of real-world applications. The authors leverage a three-step process involving GPT-4o to create a comprehensive set of assertion criteria for each prompt template. This process includes generating initial criteria, supplementing them where necessary, and refining them to remove redundancy and irrelevance. The dataset is further organized into a hierarchical taxonomy of domains and constraint categories, facilitating detailed analysis and application in prompt engineering contexts.

Benchmarking and Model Fine-Tuning

The authors establish a benchmark using PromptEvals to evaluate closed- and open-source models' ability to generate relevant assertion criteria. Models such as GPT-4o, Llama 3-8b, and Mistral-7b are assessed using metrics like semantic F1 scores, which account for the semantic similarity between generated and ground truth criteria. Findings indicate that fine-tuned versions of the Mistral and Llama 3 models significantly outperform GPT-4o, with the fine-tuned Mistral model achieving a 20.93% greater semantic F1 score, along with reduced latency and improved cost efficiency.

The fine-tuning process involves training these models on the PromptEvals dataset, leading to models that not only match but exceed the performance of larger, more resource-intensive models. This showcases the potential of finely-tuned, task-specific models, reducing the need for more expensive models in real-time production settings.

Implications and Future Directions

The implications of PromptEvals are manifold. Practically, the dataset and its associated benchmarks provide developers with tools to construct more reliable and performance-consistent LLM applications. The ability to rapidly generate and validate assertion criteria enhances LLM deployment, allowing for frequent iterations and less downtime due to misaligned outputs. Moreover, the significant improvements demonstrated by smaller, fine-tuned models advocate for streamlined models that can deliver high performance with reduced computational overhead.

Theoretically, the introduction of a robust, well-defined set of assertions for LLMs paves the way for continual improvements in LLM alignment and reliability. As prompt engineering and LLM applications expand in complexity, the need for precise and reliable output constraints becomes critical. Given this trajectory, future research could focus on expanding the dataset to include multimodal inputs, refining fine-tuning processes for even more efficiency, and exploring methods to further reduce semantic redundancy in generated criteria.

In conclusion, PromptEvals represents a substantial advancement in the landscape of LLM deployment, providing the academic and developer communities with vital resources to improve the outputs of LLMs applied in production environments. Through rigorous benchmarking and model refinement, the dataset not only addresses current challenges in LLM reliability but also sets the stage for future innovations in this rapidly evolving field.