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Expert Evaluation for Large Language Models

Unlock the actual value of your large language models.

We tackle organizations' most pressing evaluation challenges to ensure you get accurate, unbiased, and actionable insights tailored to your needs.

Expert Evaluation for Large Language Models

Trusted by top AI builders worldwide

Custom and comprehensive model comparisons

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Custom and comprehensive model comparisons

Get the full picture of how your models perform with each iteration, or against existing external models. Receive regular analyses comparing model performance across dimensions, tasks, languages, and domains depending on your needs.

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Robust evaluation frameworks

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Robust evaluation frameworks

Evaluating model performance often suffers from bias and inconsistency due to subjective opinions. Our solution ensures a fair and rigorous evaluation process through randomized model output ranking and controlled annotator behavior.

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Real data from a global network of experts

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Real data from a global network of experts

Learn how your model performs when engaging with real experts and audiences. Access one of the largest domain and language professional pools that provides top-quality feedback data.

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Precise reporting and actionable insights

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Precise reporting and actionable insights

Receive comprehensive reports that provide actionable insights into several criteria, such as domain knowledge, safety, quality, verbosity, instruction following, and more.

Stringent compliance to security requirements

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Stringent compliance to security requirements

Trusted by highly sensitive industries like defense and finance. We offer flexible deployment options to meet security requirements, including on-premise deployments and on-premise data with managed services.

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Frequent Questions

What is the purpose of LLM evaluation?

LLM evaluation aims to provide accurate, unbiased, and actionable insights into the performance of large language models by addressing common evaluation challenges and ensuring fair assessment.

How does Kili Technology ensure unbiased model evaluations?

Kili uses randomized model output ranking and controlled annotator behavior to minimize bias and ensure consistency in evaluations.

What types of reports are provided?

Comprehensive reports cover criteria such as domain knowledge, safety, quality, verbosity, and instruction following, offering actionable insights for model improvements.

Who performs the evaluations?

Evaluations are conducted by a global network of experts across various domains, ensuring high-quality and contextually accurate assessments.

What are the benefits of using Kili's evaluation service?

Benefits include receiving high-quality, precise reports quickly, reducing overhead for engineering teams, and ensuring model performance aligns with specific project needs.

How do LLM evaluation and data services work together?

LLM evaluation leverages the high-quality data generated through Kili's LLM data services. By using precise and domain-specific annotations, the evaluations are more accurate and relevant to your model's application.

What type of data can Kili provide for LLM training?

Kili offers customized data across various domains, ensuring that models receive comprehensive and contextually accurate training data suitable for their specific needs.

How does Kili handle changes in data requirements during the evaluation?

Kili's flexible and agile approach allows for adjustments in data quality definitions and volumes, ensuring that the evaluation process remains aligned with evolving project needs.

LLM Resources

A Guide to RAG Evaluation and Monitoring (2024)
A Guide to RAG Evaluation and Monitoring (202...

To ensure widespread adoption and long-term value delivery of your RAG application, following best p...

How to Build LLM Evaluation Datasets for Your Domain-Specific Use Cases
How to Build LLM Evaluation Datasets for Your...

Here's a guide to building an LLM evaluation dataset.

Building Domain-Specific LLMs: Examples and Techniques
Building Domain-Specific LLMs: Examples and T...

Discover examples and techniques for developing domain-specific LLMs (Large Language Models) in this...