HDM-2: Advancing Hallucination Evaluation for Enterprise LLM Applications
An open-source 3B parameter model that can perform contextual and common knowledge hallucination checks in language model outputs.
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HDM-2: Advancing Hallucination Evaluation for Enterprise LLM Applications
An open-source 3B parameter model that can perform contextual and common knowledge hallucination checks in language model outputs.
Introducing RRE-1: Improving RAG relevance using Retrieval Evaluation and Re-ranking
RRE-1 helps developers easily evaluate retrieval performance and allows them to fix relevance issues by applying the learnings from the evaluation in the re-ranking phase - RRE-1 can be used as a low latency re-ranker via a convenient API.
Introducing HDM-1: The Industry-Leading Hallucination Detection Model with Unrivaled Accuracy and Speed
HDM-1 delivers unmatched accuracy and real-time evaluations, setting a new standard for reliability in hallucination evaluations for open-book LLM applications.
Announcing AIMon’s Instruction Adherence Evaluation for Large Language Models (LLMs)
Evaluation methods for whether an LLM follows a set of verifiable instructions.
From Wordy to Worthy: Increasing Textual Precision in LLMs
Detectors to check for completeness and conciseness of LLM outputs.