Discover which AI application – sentiment analysis, image recognition, machine translation, or text summarization – is not typically associated with large language models. Get a detailed explanation from an Infosys Certified Applied Generative AI Professional Exam expert.
Table of Contents
Question
Which application is NOT commonly associated with large language models?
A. Sentiment analysis
B. Image recognition
C. Machine translation
D. Text summarization
Answer
B. Image recognition
Explanatio
While large language models (LLMs) have revolutionized natural language processing (NLP) tasks, they are not commonly used for image recognition. LLMs are primarily designed to understand, generate, and manipulate text data rather than visual data.
Here’s why the other options are associated with LLMs:
A. Sentiment analysis: LLMs can be fine-tuned to classify the sentiment of a given text as positive, negative, or neutral. They learn patterns and features in text data to make these predictions.
C. Machine translation: LLMs have significantly improved the quality of machine translation by learning from vast amounts of parallel text data in different languages. They can generate fluent translations while capturing context and nuance.
D. Text summarization: LLMs can generate concise summaries of long articles or documents by identifying and condensing the most important information. They are trained on large text corpora to understand and distill key points.
In contrast, image recognition typically relies on convolutional neural networks (CNNs) or other computer vision architectures specifically designed to process and analyze visual data. While multimodal models combining language and vision exist, pure language models like LLMs are not the go-to choice for image recognition tasks.
Therefore, image recognition stands out as the application not commonly associated with large language models, while sentiment analysis, machine translation, and text summarization are all areas where LLMs excel.
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