Ensuring LLM Safety for Production Applications with Shreya Rajpal - #647
Sep 18, 2023
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Shreya Rajpal, Founder and CEO of Guardrails AI, dives deep into the critical topic of ensuring safety and reliability in language models for production use. She discusses the various risks associated with LLMs, especially the challenges of hallucinations and their implications. The conversation navigates the need for robust evaluation metrics and innovative tools like Guardrails, an open-source project designed to enforce model correctness. Shreya also highlights the importance of validation systems and their role in enhancing the safety of NLP applications.
Guardrails AI provides a catalog of validators to enforce correctness and reliability of language models, addressing safety concerns such as hallucinations and violation of domain-specific constraints.
Guardrails enhances the reliability of language model outputs by providing a secondary layer of checks and validation, allowing developers to create custom correctness rules and validators specific to their industry and use case.
Deep dives
Guardrails AI: Ensuring Safety in AI Systems
Guardrails AI, founded by Shreya Rajpal, focuses on the reliable use of large language models (LLMs) in production scenarios. The company aims to address safety concerns in LLMs by enforcing correctness criteria. Hallucinations, where LLMs generate incorrect or irrelevant responses, are a major concern. Guardrails AI provides a catalog of validators that can be used to check for specific correctness criteria, such as ensuring grounding in source documents and preventing the violation of domain-specific constraints. The open-source project, Guardrails, acts as a secondary layer surrounding LLMs to ensure reliability and prevent incorrect outputs. It allows developers to create custom checks and rules specific to their use case. By running these validators and checks, developers can gain confidence in the outputs of LLMs and mitigate risks in various applications, such as chatbots, information extraction, and generating SQL queries from natural language.
Taxonomization of LLM Safety Concerns
Within LLM safety concerns, hallucinations are a major focus. Hallucinations occur when LLMs generate incorrect or irrelevant responses. Ensuring that LLMs respect domain-specific constraints, such as avoiding medical advice or brand risks, is crucial. Guardrails provides a taxonomy to classify and evaluate different types of hallucinations, such as misrepresenting entities, adding ambiguous information, or generating incorrect values. While hallucinations receive significant attention, other safety issues like performance risks, compliance risks, and brand risks also arise when LLMs are deployed at scale. Guardrails offers a framework to address these concerns, allowing developers to create custom correctness rules and validators specific to their industry and use case.
Building Confidence with Guardrails
Guardrails enhances the reliability of LLM outputs by providing a secondary layer of checks and validation. While LLMs are inherently stochastic and lack determinism, Guardrails helps establish confidence by enforcing correctness criteria. Developers can create their own validators, leveraging tools like pattern matching, high-precision classifiers, or external systems to validate and verify LLM outputs. Guardrails is designed to be integrated into the runtime pipeline, allowing for real-time assessment of outputs and continuous monitoring. The open-source project provides a catalog of pre-defined validators, but developers can also iteratively refine and customize the checks to suit their specific application needs.
Considerations for LLM Safety
Ensuring LLM safety extends beyond employing Guardrails. Developers need to consider privacy and data leakage concerns, especially when dealing with sensitive data. Guardrails offers the advantage of allowing privately hosted fine-tuned LLMs to mitigate privacy risks. Extensive offline evaluation and testing are crucial before deploying LLM workflows into production. Robust evaluation benchmarks, data curation, and monitoring ensure that the system performs reliably and meets safety criteria. Documentation of models, metrics, and use cases also contributes to a comprehensive approach to LLM safety. While Guardrails provides an essential layer of safety, developers should employ a multi-faceted strategy to address the broad spectrum of risks associated with LLM deployment.
Today we’re joined by Shreya Rajpal, founder and CEO of Guardrails AI. In our conversation with Shreya, we discuss ensuring the safety and reliability of language models for production applications. We explore the risks and challenges associated with these models, including different types of hallucinations and other LLM failure modes. We also talk about the susceptibility of the popular retrieval augmented generation (RAG) technique to closed-domain hallucination, and how this challenge can be addressed. We also cover the need for robust evaluation metrics and tooling for building with large language models. Lastly, we explore Guardrails, an open-source project that provides a catalog of validators that run on top of language models to enforce correctness and reliability efficiently.
The complete show notes for this episode can be found at twimlai.com/go/647.
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