Enhance model output through creative hallucination inducement

Uncover and correct vulnerabilities in your LLM proactively

Creative and rigorous testing

Our prompt experts challenge your model's logic from various angles and provide a comprehensive evaluation report. We conduct a multi-step assessment process, adding layers of complexity as a stress-test for model constitution.

Coding and Code Snippet-Checking

Our adversarial training techniques fortify AI models against potential adversarial attacks and misinformation with fast idenfication of susceptibilities, protecting your LLM’s from falling prey to malicious input.

Reduction of False Positives and Negatives

Our creative hallucination inducement techniques help your AI models reduce both false positive and false negative outcomes, making them more reliable in tasks such as fraud detection and medical diagnoses.

Industry-specific testing

Our model testing is not only comprehensive but also highly pertinent to the industry-related challenges your AI system may encounter.

Anti-hallucination training

After weaknesses in model output and logic have been identified, we offer corrective fine-tuning services through a multitude of human feedback techniques.

Impressions from our community

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"We were impressed with the efficiency of Pareto's team in helping us collect high-quality and challenging datasets on a tight schedule, using cost-effective and flexible processes. Their streamlined recruitment, training, and communication with the rater pool allowed us to adjust usage as needed. Their impressive versatility and ease of use throughout the process made it a pleasure to work with them."

Daniel De Freitas

Co-founder @ Character.AI

Join hundreds of fast-growing teams who count on Pareto to ensure factuality and honesty in their language models.

How it works

Built by and for a new generation of data workers

The infrastructure behind human data collection is antiquated. We’ve joined forces with seasoned data labelers, annotators, prompt engineers, and crowdwork researchers to redefine the relationship between workers and requesters.

Pareto operates on the principles of equitable compensation, collaborative management, and expert evaluation and feedback. Our mission is to empower talented and diverse professionals worldwide to contribute to AI training.

Applications of hallucination inducement across industries

Supervised training for large tech companies

Problem

A tech company faces an issue with their AI assistant as it tends to exhibit behaviors resembling those of a human. Users are expressing concerns and confusion regarding the assistant's responses. This not only hampers the user experience but also raises questions about the appropriateness of AI behavior.

Solution

1. 10,000 Distinct Prompts: To address this challenge, the company requires the creation of 10,000 diverse prompts to “break” the model into hallucinating. Identifying lines of questioning where the model breaks is the objective.
2. Ideal-Reply Demonstrations: Accompanying these prompts, the company needs demonstrations of ideal replies that subsequently guide the assistant in rejecting the notion that it possesses human-like qualities. These demonstrations serve as a blueprint for correcting and enhancing AI behavior.

Benefits

• User Comfort: By training the AI to respond in a manner that aligns with user expectations, the company can significantly enhance user comfort and trust, resulting in a more positive user experience.
• Ethical Compliance: Ensuring the AI does not mimic human traits or consciousness supports ethical AI practices and reduces the risk of user misconceptions or concerns.
• Customization: The ability to finely tune AI behavior allows the company to tailor the assistant's responses to specific user needs and industry standards.

Diagnosis assistance for imaging software

Problem

The medical company's AI model is experiencing hallucinations where it falsely imagines redness and swelling in medical images where no actual symptoms exist. This erroneous behavior could lead to incorrect diagnoses and impact the quality of medical care.

Solution

2,000 Image Annotations: To address the issue, the company requires the annotation of 2,000 medical images. These annotations need to trick the AI into discussing non-existent symptoms initially, in line with its hallucinations, and subsequently guide the model to accurately identify these symptoms as non-existent, clarifying the diagnoses.

Benefits

• Enhanced Diagnostic Accuracy: By effectively addressing the hallucination issue, the medical company can improve the accuracy of its AI model's diagnoses. This ultimately leads to more reliable medical assessments and decisions.
• Patient Safety: The correction of hallucinations prevents the AI from suggesting false symptoms, ensuring patient safety and the prevention of unnecessary treatments or interventions.
• Efficient Resource Utilization: The company's AI model can efficiently utilize its capabilities, focusing on relevant medical information, rather than generating erroneous interpretations. This results in streamlined medical processes.

These projects exemplify how model hallucination inducement is employed to tackle distinct issues, identifying weaknesses in AI to refine and avoid inadvertent misrepresentations or misunderstandings in various applications.

Enterprise-grade scale and quality

Fully managed service

Our project managers are just a Slack message or email away.

24/7 Global support

Our distributed team of experts offer assistance around the clock.

Pay-as-you-go

Up-front and transparent pricing tailored to your project requirements.

Common Questions

Ensure factual accuracy for your models

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