Aware, the AI Data Platform for workplace conversations, unveiled its performance against Meta’s latest release, Llama-2. As the tech world buzzes about Llama-2, Aware has conducted a benchmark test to compare the accuracy and cost-effectiveness of its AI models to Meta’s large language model. The results demonstrate thatย Aware‘s purpose-built platform for workplace conversations outshines industry-leadingย large language modelsย with remarkable accuracy and speed, all at a fraction of the cost.
Meta’s introduction of Llama-2 has set a newย gold standardย inย large language models, providing the market with best-in-class open-source AI. Available inย 7b,ย 13b, andย 70bย parameters, Llama-2 pushes the envelope in AI innovation and can be leveraged in countless groundbreaking applications.
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To judge the efficacy of Llama-2 out of the box, theย Awareย team ran a series of comparison tests of Llama-2-13bย against Aware’s sentiment models. In these tests, Llama-2-13bย achieved an accuracy of 62.7% inย zero-shotย sentiment determination, whereasย Aware‘s sentiment models boasted an impressive accuracy rate of 87.3%. The difference in these results lies inย Aware‘s narrowly trained, highly curated models over the one-size-fits-all approach used inย large language models. Instead of leveraging publicly available data,ย Aware‘s models are trained on a proprietary dataset of heterogenous digital workplace conversations for the express purpose of interpreting the ways communication occurs in the modern workplace. This results in more accurate and representative insights than generic datasets.
“We recognized from the very beginning that organizations would be making crucial decisions about their company and people by understanding in real-time the fluid behaviors like sentiment,” statedย Mattย Pasternack, Chief Product Officer atย Aware. “Continuously investing in the development of our ML/AI data platform, which not only exhibits industry-leading accuracy but also demonstrates remarkable adaptability to the ever-changing landscape ofย human behavior, remains a top priority for us at Aware.”
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Cost-effectivenessย remains a significant advantage of the AwareIQ platform overย larger language models. At their current volumes, the platform operates at just a fraction of the cost, with an approximate monthly expense belowย $1,000, compared to Llama-2-13b’sย $181,876ย per month. This underscores the importance of selecting tools that offer superior results while aligning with business objectives andย budget constraints.
Jason Morgan, VP ofย Data Scienceย atย Aware, emphasized the importance of understanding theย trade-offsย when businesses consider which models to adopt. He said, “You can’t deployย large language modelsย and immediately expect state-of-the-art results. We have years of experience building sentiment models tailored to specific use cases, and the results are significantly better than what you can get out-of-the-box with Llama-2. It’s crucial for businesses to understand these nuances and select the model that will give them superior results based on their needs.”
Considering these developments, Aware encourages companies to carefully evaluate factors such as performance, cost,ย scalability, and the need for fine-tuning models to align with specific use cases. The AwareIQ platform stands ready to empower businesses with the contextual intelligence needed to thrive in today’s fast-paced,ย data-drivenย world.ย The future of AI andย NLPย lies in informed choices that maximize value, andย Aware‘s platform proves to be a game-changer in achieving just that.
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