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Leverage LLMs with Confidence

Scalably assess the robustness, security and ethical performance of LLMs.

Why it matters

Large language models (LLMs) are revolutionizing the way we interact with technology. From generating creative text in various formats to translating languages, LLMs hold immense potential. However, unchecked, they can also introduce biases, generate harmful content, or perpetuate misinformation.

 

That's why testing of LLMs and its safeguards for responsible usage is crucial. QuantPi offers tailored test suites across the complete AI lifecycle from procurement to deployment of LLMs within an organization. Our test-suite can make the risks of LLMs and its safeguards evident, which in turn help to take mitigating measures and ensures that LLMs are aligned with your organization's standards.

Benefits of QuantPi’s LLM Trustworthy Assessment 

Efficient Comparability

Understand which models to adopt or procure by holistically comparing them.

Improved Fairness

Ensure that LLMs are respectful and avoid discriminatory outputs.

Enhanced Safety

Reduce the potential for bias, misinformation, and harmful content generation.

Mitigated Risks

Proactively address potential issues before they escalate.

Increased Transparency

Gain deeper insights into LLM decision-making processes.

Boosted Trust

Build user confidence and empower scalable and ethical AI adoption.

What we offer

Bias Detection and Mitigation

Identify and address potential biases within your LLM.

Explainability and Transparency

Understand how your LLM arrives at its outputs.

Content Moderation

Identify and assess the generation of harmful or toxic content.

Robustness Checks

Assess how input variations impact the system performance.

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Performance Insights

Evaluate performance with use-case specific metrics.

Security and Privacy Safeguards

Assess guardrails, including those designed to protect against prompt injection attacks. 

What you can do

Seeing is believing. So we assessed LLMs, such as Microsoft's Phi-2 and Google's Gemma 7-b, on HuggingFace and shared the results publicly to provide a better understanding of the type of insights and comparisons you can access with QuantPi's testing suite. 

Our LLM Test Suite in Action

We offer comprehensive testing across various dimensions and tailored to specific needs for any LLM or NLP use-case. Regardless of whether or not LLMs are leveraged to perform classical Natural Language Processing (NLP) tasks, QuantPi’s testing framework can be used. Examples below:
 

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Document Q&A

Performance: Evaluate how accurately the system retrieves relevant information and generates concise answers using metrics like exact matching, BLEU score, or BERTscore.


Robustness: Assess how typos and minor input variations affect the system's performance.


Security and Privacy: Assess guardrails, such as, prompt injection attacks that aim to extract sensitive information or manipulate the system.

Content Creation (e.g. Emails, Social Media Posts)

Ethics: Evaluate the likelihood of the generated content containing toxic language or inappropriate elements.


Bias and Fairness: Leverage fairness metrics like demographic parity to identify potential biases based on sensitive attributes (e.g., recipient's gender) in the generated content.

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Sentiment Analysis (e.g. Review Classification)

Performance: Assess the system's accuracy in classifying sentiment (positive, negative, or neutral) using metrics like accuracy, F1 score, and false positive rate.


Bias and Fairness: Analyze if the system performs differently for various languages or topics, ensuring fairness across subgroups.


Robustness: Evaluate how typos or minor input changes impact the system's performance.

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Summarization (e.g. Article Condensation)

Performance: Measure how well the system captures the main points and retains essential information using metrics like BLEU score or BERTscore.


Ethics: Ensure summaries remain neutral regardless of the input topic.


Robustness: Test how the system handles minor input changes like added HTML tags.

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Machine Translation (e.g. Language Conversion)

 

Performance: Evaluate the translation quality using metrics like BLEU, ROUGE-N, or METEOR scores.


Bias and Fairness: Analyze if the translation quality varies significantly between different languages, ensuring fair performance across all languages.


Robustness: Assess how typos or minor input changes impact the system's translation accuracy.

Unlock the Potential of Responsible AI

Testing is essential for organizations to leverage the power of LLMs while minimizing risks. With QuantPi’s AI Trust Platform, you can ensure that LLMs are developed and deployed ethically and responsibly.

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