Introduction
AI has advanced significantly in recent years, largely driven by the advent of Large Language Models (LLMs). These models, such as Mistral, Llama3, and Cohere, have revolutionized the way we interact with technology, enabling sophisticated conversational agents with complex problem-solving capabilities. However, despite their impressive performance, these generalist systems face inherent limitations, particularly when it comes to delivering precise and reliable information in specialized domains. Although advanced systems such as GPT4 and Perplexity have emerged, these too still fall short in providing deep insights for niche applications. This is where Apta stands out, leveraging niche expert systems to develop highly insightful features tailored to specific domains.
Background: Large Language Models
During the pre-training phase, LLMs are exposed to vast amounts of text data, enabling them to learn the statistical properties of language. Models like GPT-3 and Llama3 have been trained on diverse datasets, and develop a broad understanding of language including an understanding of grammar, semantics and can recall basic factual content. After pre-training, these models are taught to follow textual instructions, such that when prompted to tasks they are trained to align with responses that are preferred by human users. This combination of vast pre-training to gain knowledge and a strong understanding of language of knowledge followed by model alignment enables generalist LLMs to perform a wide range of tasks effectively, using simple textual prompts.
Limitation 1: Limited Parametric Memory and Hallucinations
However one of the primary limitations of generalist LLMs is their reliance on parametric memory. This means that knowledge and understanding is stored in a model’s parameters, which are fixed upon training. This means that the model cannot update its knowledge dynamically, and as a result, the information provided by the model can become outdated, particularly in evolving fields such as cryptocurrency. Furthermore, as the model was trained as a language model and to always respond to users, LLMs typically respond even when not certain of the true answer. Their generation does not rely on any factual evidence sources and instead relies directly on the complex non-linear functions defined by the model weights, and as a result, the generation process can yield hallucinated content and make non-factual statements.
Limitation 2: Limited Reasoning and Data Analysis Abilities
A further limitation of general current AI systems is the lack of ability to provide deep insights using complex reasoning and data manipulation. Even though advanced and very capable systems exist, they often are designed to factually source information and handle the risk of hallucination, and cannot provide sophisticated analysis of data. For example Perplexity is a next-generation search engine that utilizes Retrieval-Augmented Generation (RAG) to search relevant sources from the Internet, which is then processed by a generative model to produce a coherent response. Although powerful and helpful within its particular domain, Perplexity can only handle cases where the analysis is already present online, and not perform any dynamic data manipulation and analysis. Even systems such as GPT-o use agentic flow (where a query is routed to one of its many specialized submodules) and task decomposition to leverage external tools (e.g. web search or code execution), it does not have up-to-date large quantities of data that it can manipulate and query with complex queries. So in summary, though these systems can be very capable for handling general user queries and generating content or performing well-defined tasks, they will not be able to perform any complex logic and reasoning tasks, or extract any deep insights of live data within particular domains.
APTA's Distinctive Approach
Apta addresses the limitations of generalist systems by adopting a fundamentally different approach. Instead of relying on parametric memory or web searches, Apta leverages niche expert systems to generate highly insightful features tailored to specific domains.
Domain Specific Experts: Apta’s core strength lies in its ability to harness the power of niche expert systems. These systems are designed to specialize in specific domains, offering deep insights that generalist models cannot match. For example, in the cryptocurrency domain, users often seek information on whether a coin is legitimate or a potential rug pull. Apta's expert systems are equipped to analyze various factors, such as market trends, historical data, and social media sentiment, to provide a comprehensive assessment of a coin's legitimacy. These features are continually updated and preprocessed to ensure accurate and relevant information.
Deep Domain Knowledge: Unlike generalist models, which spread their capabilities across a wide range of topics, Apta focuses on developing deep expertise in specific areas. This specialization allows Apta to leverage structured databases that contain vast live data and trends, that enables highly informative and directed responses when used within particular domains. Furthermore the model is designed to have access to
textbooks and fundamental domain knowledge that enables easy retrieval of queries for particular niche concepts.
Structured Query Generation: Another key differentiator is Apta’s use of structured query generation. Having a structured querying module enables users to ask complex requests that need complex reasoning, filtering, and logic to answer. This module converts natural language prompts that would require complex data manipulation and reasoning, tasks which LLMs struggle with, and instead leans on the capabilities and abilities of structured querying systems. This method ensures that Apta can handle complex, logic-based tasks with precision.
Conclusion
While generalist LLMs like GPT-4-o and Perplexity offer remarkable capabilities, they are inherently limited in their ability to provide deep, domain-specific insights. Apta excels in niche verticals by utilizing bespoke expert systems and structured query generation to deliver highly insightful and precise responses. This specialized approach positions Apta as a superior choice for users seeking detailed and accurate information in specific domains, particularly in complex areas like cryptocurrency analysis.
Disclaimer: Some of the systems discussed in this blog post have not had their approach publicly revealed and remain a secret recipe. Therefore, some descriptions of the underlying technology may be done using partial knowledge and wide-spread hypotheses of the approaches.
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