Trustworthy Language Model (TLM)
The Problem with LLMs
Generative AI and Large Language Models (LLMs) are revolutionizing automation and data-driven decision-making. But there’s a catch: LLMs often produce “hallucinations,” generating incorrect or nonsensical answers that can undermine your business.
Introducing Cleanlab TLM
Cleanlab’s Trustworthy Language Model (TLM) is the solution. It adds a trustworthiness score to every LLM response, letting you know which outputs are reliable and which ones need extra scrutiny. TLM is a robust LLM designed for high-quality outputs and enhanced reliability—perfect for enterprise applications where unchecked hallucinations are unacceptable. Get started with our Quickstart Tutorial to explore our Python API or read more about our research here.
Key Features
- Trustworthiness Scores: Each response comes with a trustworthiness score, helping you reliably gauge the likelihood of hallucinations.
- Higher Accuracy: Rigorous benchmarks show TLM consistently produces more accurate results than other LLMs like GPT-3.5 and GPT-4.
- Scalable API: Designed to handle large datasets, TLM is suitable for most enterprise applications, including data enrichment, extraction, and validation.
Unlocking Enterprise Use Cases
With TLM, the possibilities are broader than ever before:
- Chatbots. TLM tells you which LLM API outputs you can use directly (refund, reply, auto-triage) and which LLM API outputs you should escalate for human review based on the trustworthiness score for each output. With standard LLM APIs, this is not possible, because there is no reliability score for the outputs. Explore the tutorial
- Auto-Labeling: Streamline your data annotation process. TLM auto-labels data with high accuracy, requiring human review only for outputs with low trust scores. Explore the tutorial
- Extraction. TLM tells you which data auto-extracted from PDF, PPTs, and large text repos is reliably extracted and which should be double checked for review, enabling teams to generate product catalogs, convert unstructured text to structured tabular data, and more, with 90% less time spent reviewing outputs. Explore the tutorial
- Retrieval-Augmented Generation. TLM tells you which RAG responses are unreliable by providing a trustworthiness score for every RAG answer relative to a given question, enabling you to automate high trust data and review low trust data, cutting average cost down by 90%. Explore the tutorial
Proven Impact on Enterprise Deployment
Cleanlab TLM can be integrated into your existing LLM-based workflows to improve accuracy and reliability. With a trustworthiness score for each response, you can manage the risks of LLM hallucinations and avoid costly errors.
Turn your LLM into a TLM today
It’s free to try, with no credit card required.