Leverage GenAI with Confidence
Scalably assess the robustness, security and ethical performance of GenAI
Why it matters
Generative AI (GenAI) is revolutionizing the way we interact with technology. From generating content across various formats – including text, images, audio, and code – to transforming data and enabling novel forms of expression, GenAI holds immense potential. However, unchecked, they can also introduce biases, generate harmful content, or perpetuate misinformation.
That's why testing GenAI and its safeguards for responsible usage is crucial. QuantPi offers tailored test suites across the AI lifecycle from procurement to deployment of diverse GenAI models within an organization. Our test-suite makes hidden risks evident, which in turn helps to take mitigating measures and ensures that GenAI models are not only high performing but also aligned with your organization's standards.
What we offer
Bias Detection and Mitigation
Identify and address potential biases within your LLM.
Content
Moderation
Identify and assess the generation of harmful or toxic content.
Performance
Insights
Evaluate performance with use-case specific metrics.
Explainability and Transparency
Understand how your LLM arrives at its outputs.
Robustness
Checks
Assess how input variations impact the system performance.
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 GenAI Test Suite in Action
We offer comprehensive testing across various dimensions and tailored to specific needs for any GenAIÂ model, below we look at a few LLM / NLP use-case examples.
.png)
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.
.png)
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.
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.
.png)
.png)
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.