Core functionality for TLM
Project description
Trustworthy Language Model (TLM)
The Trustworthy Language Model scores the trustworthiness of outputs from any LLM in real-time.
Automatically detect hallucinated/incorrect responses in: Q&A (RAG), Chatbots, Agents, Structured Outputs, Data Extraction, Tool Calling, Classification/Tagging, Data Labeling, and other LLM applications.
Use TLM to:
- Guardrail AI mistakes before they are served to user
- Escalate cases where AI is untrustworthy to humans
- Discover incorrect LLM (or human) generated outputs in datasets/logs
- Boost AI accuracy
Powered by uncertainty estimation techniques, TLM works out of the box, and does not require:
data preparation/labeling work or custom model training/serving infrastructure.
Learn more and see precision/recall benchmarks with frontier models (from OpenAI, Anthropic, Google, etc):
Blog, Research Paper
Usage
See notebooks for Jupyter notebooks with example usage.
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