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.
High-quality, truthful data is crucial for developing trustworthy AI systems. Turing uses a comprehensive methodology, leveraging reinforcement learning from human feedback (RLHF) to optimize your model’s factuality performance. Additionally, our model validation techniques provide an in-depth assessment of factual integrity, ensuring LLMs generate high-confidence, evidence-backed responses.
Our experts assess your project’s complexity, volume, and effort, ensuring a tailored approach. LLM validation is integral to our evaluation, guaranteeing high factuality standards.
Using our vetted technical professionals, we build your fully managed team of model trainers, reviewers, and more—with additional customized vetting, if necessary.
You focus solely on task design while we handle coordination and operation of your dedicated training team, incorporating strategies for reducing hallucinations in LLMs through structured and evidence-based learning.
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.
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.
“Turing’s 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.”
Talk to one of our solution architects and start your factuality training project.
LLM 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.
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.
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.
Businesses can benefit from using factually accurate models in several ways:
Turing addresses potential biases in LLM factuality by implementing a comprehensive bias detection and mitigation strategy. Our approach includes: