前 5 大语言模型以及如何有效使用它们.docx

Top 5 Large Language Models and How to Use Them Effectively

LLMs hold the key to generative AI, but some are more suited than others to specific

tasks. Here's a guide to the five most powerful and how to use them.

Modern Large Language Models (LLMs) are pre-trained on a large corpus of self-

supervised textual data and are then tuned to human preferences via techniques such as

reinforcement learning with human feedback (RLHF).

LLMs have seen rapid advances over the last decade or so, and particularly since the

development of GPT (generative pre-trained transformer) in 2012. Google’s BERT, introduced

in 2018, represented a significant advance in capability and architecture and was followed by

Given how new this all is, we’re still getting to grips with what may or may not be possible

with the technology. But the capabilities of LLMs are undoubtedly remarkable, with a wide

range of potential applications in business. These include being used as chatbots in customer

support settings, code generation for developers and potentially business users as well, audio

transcription summarizing and paraphrasing, translation, and content generation.

You can imagine, for example, customer meetings could be both transcribed and

summarized by a suitably trained LLM in near real-time, with the results shared with the sales,

marketing and product teams. Or an organization’s web pages might automatically be

translated into different languages. In both cases, the results would be imperfect but could

be quickly reviewed and fixed by a human reviewer as needed.

particular industries. “In wealth management,” Madhukar Kumar, CMO of SingleStore, a

relational database company, told the New Stack, “we are working with customers who have

a huge amount of unstructured data, such as legal documents stored in PDFs, and want to

be able to query them in plain English using a Large Language Model.”

SingleStore is seeing clients using LLMs to perform both deterministic and non-

deterministic querying at the same time.

“ For example, in wealth management, I might want to be able to say, ‘Show me the

income statements of everybody between 45 and 55 years old who recently quit their job,’

because I think they are right for my 401(k) product,” Kumar said.

“This requires both database querying via SQL and the ability to work with that corpus