The most trusted LLM factuality training experts

Improve your model
factuality

Enhance your model’s accuracy and reliability with our advanced LLM factuality services, including fact verification, bias and misinformation detection, and source credibility assessment, ensuring your model consistently delivers truthful and credible information.

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Accurate data, trusted AI

High-quality, truthful data is crucial for developing trustworthy AI systems. Manovega uses a comprehensive methodology, leveraging reinforcement learning from human feedback (RLHF) to optimize your model’s factuality performance.

Factuality training

specialties

Fact verification and correction

Ensure your model consistently delivers accurate information by verifying and correcting facts, minimizing the spread of misinformation.

Source credibility assessment

Improve your model’s ability to assess the credibility of sources, ensuring it uses and provides trustworthy information.

Consistency and coherence checking

Enhance the coherence and consistency of your LLMs’ outputs, ensuring logical and factual alignment across all responses.

Real-time fact-checking integration

Implement real-time fact-checking capabilities to verify information on-the-fly, enhancing your model’s reliability in dynamic environments.

Bias and misinformation detection

Detect and mitigate bias and misinformation in your model’s data sources and outputs, ensuring unbiased and truthful responses.

Truthfulness and integrity assurance

Guarantee the integrity and truthfulness of your model’s responses by training it to recognize and adhere to factual standards.

LLM factuality training

starts here

Model evaluation and analysis

Our in-house solutions architects and experts help you scope your project for task complexity, volume, and effort.

Team identification and assembly

Using our vetted technical professionals, we build your fully managed team of model trainers, reviewers, and more—with additional customized vetting, if necessary.

Factuality training task design and execution

You focus solely on task design while we handle coordination and operation of your dedicated training team.

Scale on demand

Maintain consistent quality control with iterative workflow adaptation and agility as your training needs change.

Enhance your model’s accuracy and reliability. Talk to one of our solutions architects today.

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Cost-efficient R&D for

LLM training and development

Empower your research teams without sacrificing your budget or business goals. Get our starter guide on strategic use, development of minimum viable models, and prompt engineering for a variety of applications.

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“Manovega ability to rapidly scale up global technical talent to help produce the training data for our LLMs has been impressive. Their operational expertise allowed us to see consistent model improvement, even with all of the bespoke data collection needs we have.”

Operations Lead
World’s leading AI lab.

How does your model measure?

Talk to one of our solution architects and start your large language model performance evaluation.
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Frequently asked

questions

Find answers to common questions about training and enhancing
high-quality LLMs.
What is model factuality and what is it important?

Model factuality refers to the accuracy and truthfulness of the information generated by a large language model (LLM). Ensuring factuality is crucial because it enhances the reliability and credibility of LLM-generated content, helping users make informed decisions and reducing the spread of misinformation. While factuality is important for every industry, it is particularly critical in sectors like finance, healthcare, and legal matters.

How can Manovega help improve the factual accuracy of my LLM model?

At Turing, we leverage a comprehensive methodology that includes rigorous fact verification, bias and misinformation detection, source credibility assessment, real-time fact-checking integration, consistency checks, and reinforcement learning from human feedback (RLHF) to continuously optimize your models’ factuality performance. Additionally, we have human experts in various domains, including STEM professionals, to review and validate content, ensuring domain-specific accuracy.

Can LLM models be tailored to provide industry-specific factual content?

Yes, at Turing, we offer enterprise LLM factuality training solutions customized to meet the specific needs of different industries. Our model integration support ensures that the LLMs deliver accurate and truthful information relevant to various domains, enhancing their applicability and usefulness for industry-specific applications.

How can businesses benefit from using factually accurate LLM models?

Businesses can benefit from using factually accurate LLM models in several ways:

  • Access to reliable and accurate information helps businesses make well-informed decisions.
  • Reducing misinformation minimizes potential legal and reputational risks.
  • Providing factual content builds trust with customers, partners, and stakeholders.
  • Automating fact-checking and bias detection processes streamlines workflows and reduces the need for manual oversight.
How does Manovega address potential biases in LLM factuality?

Turing addresses potential biases in LLM factuality by implementing a comprehensive bias detection and mitigation strategy. Our approach includes:

  • Bias detection: Identifying biases in the model outputs.
  • Mitigation techniques: Applying algorithms and methodologies to reduce or eliminate detected biases.
  • Diverse data sourcing: Using a wide range of high-quality data sources to ensure balanced and unbiased training.
  • Human feedback: Leveraging feedback from diverse human reviewers to refine and improve the model’s performance.
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