Are Large Language Models the Ultimate Solution? Exploring the Limitations and Constraints of LLMs.
The recent emergence of Large Language Models (LLMs) has transformed the field of AI, ushering in a new era of general models with unprecedented natural language processing (NLP) capabilities. These advanced models, based on the revolutionary transformer architecture, are typically trained on massive quantities of textual data and therefore gain impressive world knowledge, as well as a deep understanding of language. As a result, LLMs demonstrate remarkable fluency and versatility in generated text, producing highly convincing and immersive texts. As a result, LLMs have been used in a wide range of applications including as writing assistants, as information sources and as conversational agents, continually expanding the boundaries of what machines can achieve in language understanding and generation.
Linkedin 2023 Large language models (LLMs) have a wide range of use cases across various industries and fields. Communication & interaction, content creation, data analysis & insight, research & education, and much more.
Despite their impressive capabilities, LLMs possess significant limitations that prevent them from being the definitive solution for all NLP tasks. One of their most critical drawbacks is their tendency to "hallucinate" information. Hallucination describes the scenario when generated outputs, while seemingly credible, are factually incorrect and are either inconsistent with the provided context or contradict established factual knowledge. LLMs prioritize textual coherence and are not designed to verify the truthfulness of their outputs, and will therefore generate responses even when the model doesn’t know the correct answer.
Furthermore, once trained, LLMs only maintain the static information that was present at the time of training. They do not update to incorporate new knowledge, which means they lack information about events or developments that occurred post-training. Additionally, LLMs are trained across broad domains, and possess reasonable general knowledge in many domains, however may not have expert knowledge in any particular domain. If applied to particular niche domains, such as the crypto domain, the systems may only possess limited knowledge and may not be aware of any technical details. In such cases, proprietary data can be instrumental in enhancing model performance and enabling practical assistance in these specific domains.
Therefore, while LLMs are highly effective and yield many impressive capabilities, they alone are not sufficient to build highly capable search engines that fully meet practical requirements. At APTA, we are leveraging state-of-the-art AI research to support and augment the capabilities of LLMs, such as enabling queries for real-time information and ensuring responses are generated on well established factual information sources within specific domains. Our aim is to create highly effective, reliable, and comprehensive AI-assisted vertical-specific search engines that surpass traditional capabilities and better serve the practical needs of users.
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