The Future of Large Language Models (LLMs): Powering Data Scientists and Programmers Rather than Replacing Them

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The advent of Large Language Models (LLMs) like GPT-4 and Bard have ushered in a new era of technological capability. While their influence has been largely hailed as democratising?language generation and analysis capability, I want to challenge this narrative. The true power of LLMs, I argue, gives data scientists and programmers an even larger edge compared to the regular user.?

Why? For 5 reasons:

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1. The Art of Superior Prompting?

When communicating with LLMs, the way you phrase your input (or "prompt") matters. Advanced techniques have emerged, such as chain of thought, graph prompting, and react prompting.?

In a 'chain of thought' method, prompts are built to be coherent and in sequence, helping the model better understand the context. Graph prompting turns the interaction into a conversation network, making the model more powerful in extracting and using information from previous prompts. React prompting is a technique where responses are given to the LLM, not as user input, but as the model's own thoughts, allowing it to react to its own output.

The most recent in these advanced prompting methods, Progressive-Hint Prompting (PHP), has been introduced to enable automatic multiple interactions between users and LLMs by using previously generated answers as hints to progressively guide towards the correct answers.

Combining CoT, self-consistency, and PHP can significantly improve accuracy, with experimental results showing a 4.2% improvement on GSM8K with greedy decoding compared to Complex CoT, and a 46.17% reduction in sample paths with self-consistency using text-davinci-003. 1 ?

Thus greatly improving, the quality of output, but understanding and implementing them effectively requires a deep understanding of LLMs' working principles - something well within the purview of data scientists and AI programmers.?

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2. Harnessing APIs for Higher Throughput?

The standard user interface is user-friendly but limited in throughput capacity. For businesses and applications demanding more, APIs are the answer. They allow for much higher throughput, integration into existing workflows, and the ability to handle batch processing tasks.??

Research has shown that over a four-year period, firms using APIs saw 12.7% more growth in market capitalization compared to those that did not adopt APIs. APIs allow access to a business’s most valuable data and functionality which means companies can more efficiently reuse internal capabilities, share assets, and co-innovate with partners 2 .?

But leveraging APIs effectively necessitates a degree of programming knowledge, further emphasising the advantage of data scientists and programmers in the LLM era.?

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3. Logic Design: A Programmer's Playground?

Despite the advances in AI, complex logic design is still an area where human programmers excel. While LLMs can generate human-like text, creating intricate, reliable, and functional code is a different story.?

Programmers are uniquely equipped to understand and implement complex logic structures. In the hands of these professionals, LLMs become powerful tools for generating pseudocode or brainstorming program logic - a symbiotic relationship that doesn't replace programmers, but rather augments their capabilities.?

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4. Querying Proprietary Data?

The combination of LLMs with technology like Langchain and Picecone, alongside OpenAI, enables querying proprietary data and integrating the responses into an LLM's outputs. This amalgamation of technologies is a boon for companies that want to personalize and add a layer of contextual understanding to their AI interactions.?

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However, this approach requires a deep understanding of data structuring, indexing, API design, and LLM interaction - again, skills typically held by data scientists and programmers.?

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5. Debugging and Model Tuning?

Last but not least, while LLMs are impressive, they're not perfect. They can produce flawed or biased output, or misinterpret prompts. Thus, the need for debugging and model tuning is paramount.?

This process involves understanding the inner workings of the model, identifying weaknesses, and implementing solutions. It's a task that is not only technical but also requires a degree of creativity and critical thinking - strengths often found in experienced data scientists and programmers.?

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In conclusion, the advent of Large Language Models doesn't democratize language capability development as much as it empowers those with an existing knowledge of data science and programming. The technical complexity, subtlety, and depth of understanding needed to leverage these tools effectively remains a barrier for the general public. It seems that, for the time being at least, LLMs are poised to be another powerful tool in the arsenal of data scientists and programmers, rather than their replacement.?

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We at?i-Genie.AI?have leveraged?LLM?in numerous ways to create products that helps our clients at scale.?Interested? [email protected]