LLM use cases and tasks

https://www.coursera.org/learn/generative-ai-with-llms/lecture/7zFPm/llm-use-cases-and-tasks

LLM use cases and tasks - Week 1 | Coursera

While large language models (LLMs) and generative AI are often associated with chatbots, they are versatile tools that can be used for various text generation tasks. LLMs are built on the concept of next-word prediction, which forms the basis of chatbots but can be extended to a range of applications:

 

Essay Writing: LLMs can generate essays based on prompts, providing written content on specific topics.
Conversation Summarization: By inputting dialogues as prompts, LLMs can summarize conversations, leveraging their natural language understanding.

Translation: LLMs can perform translation tasks, translating between different languages or even translating natural language into machine code for tasks like coding.

Information Retrieval: LLMs excel at named entity recognition, identifying people and places in text, aiding in information retrieval.

Integration with External Data: Developers are exploring ways to connect LLMs to external data sources or APIs to expand their knowledge and real-world interactions.

As LLMs scale from millions to billions of parameters, their language understanding and capabilities increase. Smaller models can also be fine-tuned for specific tasks. The architecture of LLMs has been instrumental in their rapid development
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대규모 언어 모델 (LLMs)과 생성적 AI는 종종 챗봇과 관련이 있다고 생각할 수 있지만, 이들은 다양한 텍스트 생성 작업에 사용할 수 있는 다재다능한 도구입니다. LLMs는 다음 단어 예측 개념에 기반을 두고 있으며, 챗봇의 기초가 되지만 다양한 응용 프로그램으로 확장할 수 있습니다.

에세이 작성: LLMs는 프롬프트를 기반으로 에세이를 생성할 수 있어 특정 주제에 대한 글을 제공합니다.

대화 요약: 대화를 프롬프트로 입력하여 LLMs가 대화를 요약하고 자연어 이해를 활용합니다.

번역: LLMs는 번역 작업을 수행할 수 있어 다른 언어 간의 번역 뿐만 아니라 코딩과 같은 작업을 위해 자연어를 기계 코드로 번역할 수 있습니다.

정보 검색: LLMs는 이름 개체 인식에서 뛰어나며 텍스트에서 사람과 장소를 식별하여 정보 검색을 지원합니다.

외부 데이터 통합: 개발자들은 LLMs를 외부 데이터 소스 또는 API에 연결하여 지식을 확장하고 현실 세계 상호 작용을 활성화하는 방법을 탐구하고 있습니다.

LLMs가 수백만 개에서 수십억 개의 매개변수로 확장되면 언어 이해와 능력이 증가합니다. 작은 모델도 특정 작업에 대한 미세 조정이 가능합니다. LLMs의 아키텍처가 그들의 신속한 발전에 중요한 역할을 해왔습니다.