?????? ?????? ???????? ?????? ???????????????????????????? ?????? ???? ???????????????? ???? ????????-?????????
In a recent post, I talked about why hallucinations happen in LLMs and how they affect different AI applications. While creative fields may welcome hallucinations as a way to spark out-of-the-box thinking, business use cases don’t have that flexibility. In industries like healthcare, finance, or customer support, hallucinations can’t be overlooked. Accuracy is non-negotiable, and catching unreliable LLM outputs in real-time becomes essential.
So, here’s the big question: ?????? ???? ?????? ?????????????????????????? ?????????????? ?????? ?????????????????? ???? ?????????????? ???? ?????????????????????????????
That’s where the ?????????????????????? ???????????????? ?????????? (??????) steps in. TLM helps you detect LLM errors/hallucinations by scoring the trustworthiness of every response generated by ?????? LLM.? This comprehensive trustworthiness score combines factors like data-related and model-related uncertainties, giving you an automated system to ensure reliable AI applications.
?? The benchmarks are impressive. TLM reduces the rate of incorrect answers from OpenAI’s o1-preview model by up to 20%. For GPT-4o, that reduction goes up to 27%. On Claude 3.5 Sonnet, TLM achieves a similar 20% improvement.
Here’s how TLM changes the game for LLM reliability:
1?? For Chat, Q&A, and RAG applications: displaying trustworthiness scores helps your users identify which responses are unreliable, so they don’t lose faith in the AI.
2?? For data processing applications (extraction, annotation, …): trustworthiness scores help your team identify and review edge-cases that the LLM may have processed incorrectly.
3?? The TLM system can also select the most trustworthy response from multiple generated candidates, automatically improving the accuracy of responses from any LLM.
With tools like TLM, companies can finally productionize AI systems for customer service, HR, finance, insurance, legal, medicine, and other high-stakes use cases.? Kudos to the Cleanlab team for their pioneering research to advance the reliability of AI.
I am sure you want to learn more and use it yourself, so I will add reading materials in the comments!
