Perplexity Introduces Online LLMs With Real-Time Information
🚀 Perplexity Unveils New LLMs
– San Francisco-based startup Perplexity introduces two online large language models (LLMs): pplx-7b-online and pplx-70b-online.
🔄 Key Advantages Over Existing LLMs
– Addresses limitations like outdated information and inaccurate facts common in many LLMs.
– Utilizes real-time internet data for responses, distinguishing from offline models like GPT-3.5.
🌐 What Are Online LLMs?
– Perplexity’s online LLMs access the latest information from the internet for dynamic responses.
– Capable of reporting recent sports scores, stock prices, and Google news developments.
🤖 Enhancing Factual Accuracy
– Incorporates techniques to maximize accuracy and minimize false information.
– Built on mistral-7B and llama2-70B models, fine-tuned on diverse, high-quality datasets.
🔍 In-House Search Technology
– Perplexity employs an in-house search, indexing, and crawling infrastructure for the most relevant and up-to-date information.
– Sophisticated algorithms ensure high-quality, non-SEOed sites are prioritized.
📊 PPLX vs. GPT 3.5
– Early tests indicate PPLX models match or exceed GPT-3.5 across robustness, helpfulness, and knowledge benchmarks.
– PPLX’s real-time access to online information enhances its capabilities.
📑 Overcoming Accuracy Challenges
– “FRESHLLMS” paper underscores traditional LLM limitations, proposing FRESHQA benchmark and FRESHPROMPT method.
– Perplexity’s online LLMs effectively address challenges of freshness and accuracy highlighted in the paper.
🌐 Accessibility of PPLX Models
– Accessible via Perplexity’s API and Labs web interface for developers.
– Marks a significant step for democratizing AI, according to Perplexity CEO Aravind Srinivas.
🌐 Conclusion:
– Perplexity’s models bridge the gap between large tech firms and smaller organizations, providing access to the latest web insights.
– Envisions a future where AI assistants offer human-like responses with timely, factual, and nuanced information.
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