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Correcting Hallucinations in Large Language Models
Research

Correcting Hallucinations in Large Language Models

In this blog post, we share the results of our initial experiments aimed at correcting hallucinations generated by Large Language Models (LLMs). Our focus is on the open-book setting, which encompasses tasks such as summarization and Retrieval-Augmented Generation (RAG).

Utkarsh JainSuleman KaziOfer Mendelevitch
Utkarsh Jain,Suleman Kazi,Ofer Mendelevitch
Mockingbird is a RAG-Specific LLM that Beats GPT 4, Gemini 1.5 Pro in RAG Output Quality

Mockingbird is a RAG-Specific LLM that Beats GPT 4, Gemini 1.5 Pro in RAG Output Quality

In response to growing enterprise concerns over data security and the quality of retrieval-augmented generation (RAG), Vectara is proud to introduce Mockingbird, an LLM fine-tuned specifically for RAG. Mockingbird achieves the world’s leading RAG output quality and hallucination mitigation, making it perfect for enterprise RAG and autonomous agent use cases.

Nick MaSuleman Kazi
Nick Ma,Suleman Kazi
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