Cleanlab’s Trustworthy Language Model (TLM)` is a technology that overcomes the biggest barrier to enterprise adoption of LLMs, which is hallucinations and reliability. By adding a trust score to every LLM response, TLM lets you contain and manage bogus LLM outputs, enabling you to deploy generative AI for new use cases previously unsuitable for LLMs. Through rigorous benchmarking, we’ve shown that TLM both produces more accurate outputs than existing LLMs and has better-calibrated trustworthiness scores (enabling greater cost/time savings) than other common approaches to taming LLM uncertainty. TLM works by augmenting existing LLMs with a layer of trust, which can be used as a drop-in replacement for your LLM or even as a layer of trust for your existing LLM outputs or human-generated data. The technology has been shown to reduce error rates in various benchmark datasets and improve the accuracy of responses from popular LLMs such as GPT-4, GPT-4o, GPT-4o mini, and Claude 3 Haiku. Additionally, TLM can be used for open-domain data extraction, auto-labeling, customer service chatbots, and other applications that require reliability and trustworthiness in AI outputs.