Vectara Launches Boomerang: The Next-Gen Large Language Model Redefining GenAI Accuracy

Outpacing Major Competitors, Boomerang Sets a New Benchmark in Grounded Generative AI for Business Applications: Mitigating Hallucinations and Copyright Concerns, Minimizing Bias, Enhancing Explainability, and Broadening Cross-Lingual Reach

Large Language Model (LLM) builder Vectara, the trusted Generative AI (GenAI) platform unveiled Boomerang, a next-gen neural information retrieval model integrated into its end-to-end GenAI platform. In recently published benchmarks Boomerang outperforms Cohere and is comparable to OpenAI on certain performance metrics, expressly excelling at multilingual benchmarks. As a security-first AI leader, Boomerang significantly reduces the probability of bias, copyright infringement and “hallucinations,” fabricated information or inconsistencies in model outputs that have become an industry-wide problem and critical challenge for business adoption.

Vectara’s early “Grounded Generation” paved the way for mitigating hallucinations, a practice many others are adopting via the moniker Retrieval Augmented Generation. Boomerang takes this a step farther. Vectara’s ML team designed Boomerang from the ground-up to deliver the most accurate neural retrieval with low latency and increased cross lingual support to hundreds of languages and dialects.

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The new Boomerang model:

• Exceeds Cohere and is comparable to OpenAI in multidomain benchmark testing, allowing users to operationalize powerful GenAI without having to finetune a foundational model with their own data.
• Is capable of simultaneously understanding content in hundreds of languages and dialects, capturing 99.9% of dialects spoken by the human population, overcoming company language barriers, and outperforming other larger models in cross-lingual retrieval.
• Better understands the user’s prompt intent, leading to more accurately retrieved facts from the customer’s data; this leads to higher quality generated responses which enables users to reach their end goal faster.
• Reduces request latency for query execution up to 20%, decreasing time spent by user’s time to retrieve generative insights.
• Provides better thresholding to more reliably differentiate between relevant and irrelevant results, which increases the reliability of generated responses.

GenAI has many meanings – including generating text, images, and video. Vectara’s Boomerang is part of a trusted text generative AI ecosystem which offloads the retrieval augmented generation pipeline complexity from builder workloads via easy-to-use APIs. This API simplification empowers developers to quickly, and securely build AI applications, including question answering and conversational AI (ChatBots) from user provided data sets. Vectara’s zero-shot model never trains on user data and matches the user intent based on deep understanding of the prompts they are issuing as correlated with the input data sets. The relevant answers are generated via natural language summaries with citations to the data source facts, which provides explainability for the produced responses; a necessary requirement for using generative AI in regulated industries.

“At Vectara, we aim to solve the biggest problems facing Generative AI adoption today,” said Amin Ahmad, Cofounder and CTO of Vectara. “Our neural retrieval model achieves state of the art relevance across hundreds of languages and dialects, significantly reducing one of the biggest barriers to responsible AI adoption in the enterprise: hallucinations.”

It’s now incumbent upon nearly every enterprise to leverage and integrate GenAI into their operations and offerings—60% of organizations with reported AI adoption are already using GenAI. But until now, there was little guarantee that the information provided by LLMs would be accurate or even consistent. Now, Vectara’s Boomerang enhances the platform’s ability to bring the paradigm-shifting GenAI capabilities to nearly every organization and developer in a cost-effective, easy-to-use, and secure manner, while substantially mitigating the risks and downsides.

Integrating GenAI is business-critical for nearly every enterprise organization. Vectara has built its own model to deliver GenAI solutions to enterprise orgs that are cost-effective, secure, and reliable.

“Companies struggle to generate trustworthy content with their generative AI initiatives. In fact, 58% of respondents to a recent survey by Eckerson Group said inaccurate outputs are the greatest risk of language models, more than triple any other risk,” said Kevin Petrie, VP of Research at Eckerson Group. ”Vectara’s approach of grounded generation, also known as retrieval augmented generation, reduces this risk by querying companies’ domain specific data as it responds to user prompts. This offers a cost-effective alternative to fine-tuning language models on companies’ data.”

To showcase its momentum, Vectara will be at the AI After Dark Networking Mixer and AI Startup Showcase as part of the AI Conference. Visit Ofer Mendelevitch, Head of Vectara’s Developer Relations, on Tuesday 9/26 at 5 p.m. PT at the San Francisco event to learn more about its new model and solutions available to enterprises

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