Cleanlab

Don’t want users to lose trust in your RAG system? Then add automated hallucination detection. Just Published: A comprehensive benchmark of hallucination detectors across 4 public RAG datasets, including: RAGAS, G-eval, DeepEval, TLM, and LLM self-evaluation. See how well these methods actually work in practice for automatically flagging incorrect RAG responses: https://lnkd.in/gq6HiAds

When building AI Assistants for automated Yes/No decisions, controlling false positive and false negative error rates is crucial. Trustworthiness scores provide a reliable confidence estimate, ensuring the assistant only predicts Yes when it’s confident in the decision. For example, if predicting Yes could have a much higher cost than predicting No, the assistant will choose No unless it's highly confident that Yes is the correct choice. Additionally, the assistant can be set to say Unsure instead of making a risky decision. Learn more: ???https://lnkd.in/gxDDHKwr

Don't let your RAG system hallucinate. Hui Wen Goh's article benchmarks different methods for detecting hallucinations in #LLM-generated responses, helping you build more reliable #RAG applications.

For AI assistants that select their responses from a predefined list of categories, trustworthiness scores for class predictions can boost accuracy without changing the prompts or model. In a legal document categorization task, we reduced the error rate of OpenAI's GPT-4o classifications by 33% and reached 100% classification accuracy by escalating untrustworthy outputs to humans. Learn more: ?? https://lnkd.in/g_kUmP4C

When LLMs are being released quick we add them to Cleanlab quick as well — OpenAI's gpt-4.5-preview is now supported! Note how even the smallest punctuation decisions can lead to significantly different results, even on gpt-4.5-preview.

Anthropic's new Claude 3.7 Sonnet pushes the boundaries of AI capabilities. Yet, even the best models can still hallucinate. That's where trustworthiness scoring comes in—evaluating responses from any model, including Claude 3.7 Sonnet.

???? Improve your AI agents' accuracy in customer support escalations Building AI-driven support? TLM keeps your AI responses accurate, trustworthy, and policy-aligned. Our latest demo covers: ?? Evaluating AI responses to customer inquiries in real-time ?? Scoring requests against return policies to detect inconsistencies ?? Routing decisions—when to send responses directly vs. escalate to human agents ?? Watch the demo: https://lnkd.in/g6wcuV67

Trustworthy language models (TLMs) are a concept introduced by Cleanlab that aim help with flagging LLM responses with high uncertainty to help catching hallucinations. Let’s understand how it works. ? What’s the problem? Detecting LLM hallucinations on the fly in real-time and providing a trustworthiness score for each LLM response. ???? TLM A TLM is a wrapper around any language model. The wrapper then helps with computing the trustworthiness score of an LLMs response by computing two different scores: 1. Observed Consistency 2. Self-Reflection ?? Observed Consistency 1. Sample a response to the original query from the LLM. 2. Create multiple versions of the original query. 3. Sample k responses to each of the altered versions of a query. 4. Use NLI to assess whether the response to the original query and altered queries entail each other or not. 5. Aggregate NLI scores The use of NLI accounts for validating the semantic similarity of responses. ?? Self Reflection Ask the LLM whether its response to the original query was correct, not sure or incorrect. Wrap Up They then aggregate the two scores together to come up with a final trustworthiness score. The trustworthiness score can be introduced in various tasks to bring in a human in the loop like: 1. Flag AI labelled datasets for potential corrections. 2. Flag RAG responses that might have hallucinations. 3. Prompt a human to intervene in an AI workflow. For more read: https://lnkd.in/e5CNYcPq

?? How to enhance the accuracy of AI agents in customer support TLM (Trustworthy Language Model) accurately scores generative AI responses, improving trust and reliability in AI-driven support. Our latest tutorial highlights how TLM helps customer support: ?? Ensure AI responses are reliable and policy-compliant ?? Confirm order status from recent purchases ?? Extract customer information & order details for accuracy ?? Classify conversations for better analysis and routing Watch the tutorial: https://lnkd.in/gcYWppgJ

Umair Ali Khan introduces a new method for evaluating #LLM reliability. He demonstrates how to use a trustworthy language model to assess the trustworthiness of other #LLMs.