NLP vs. LLM: An In-Depth Comparison

Natural language processing (NLP) and large language models (LLMs) are two distinct approaches that are transforming how humans interact with machines. Both are redefining what’s possible when human communication meets machine understanding. However, is one approach really better than the other?  

NLP focuses on focused tasks like sentiment analysis and text translation, often using smaller models or rule-based systems. On the other hand, LLMs use massive datasets and deep learning to handle diverse, complex tasks like conversational AI and creative writing, providing scalability and versatility.

This blog addresses the NLP vs. LLM debate by discussing what they are, their differences, and their use cases.

NLP vs. LLM at a Glance

What is Natural Language Processing (NLP)? 

NLP is a branch of artificial intelligence (AI) that aims to train machines to read, understand, interpret, and respond to human language. It connects regular human language with machine data using a combination of AI, computer science, and computational linguistics. These algorithms first identify the patterns in data and then convert this data into a format that computers can work with. 

There are three core components of NLP: 

• Syntax Analysis enables machines to understand sentence structure.
• Semantic Analysis interprets the meaning of a text. 
• Sentiment Analysis assesses the emotions or opinions expressed in a text.

What are Large Language Models (LLMs)? 

LLMs are AI models that can understand and generate humanized text. They are trained using large, high-quality datasets with billions and billions of words from books, websites, articles, and other online sources of text. LLMs go further than interpreting human language. These models are built to predict what a person will say next based on what they’ve said before. 

The major components of an LLM include the following: 

• Tokenization breaks text down into smaller units (tokens).
• Embedding is a representation of a token that contains semantic information and encodes the relationships between different tokens, providing context to the model.
• Attention mechanisms, specifically self-attention, analyze inter-token relationships to determine the relevance and importance of different words relative to each other. 
• Pretraining provides LLMs with knowledge and language samples, enabling them to learn grammar and retain facts. 
• Fine-tuning is targeted training using particular tasks or datasets, which improves the LLM’s performance in a particular context.

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LLM vs. NLP: Key Differences 

1. Data Size and Task Scope 

NLP: Typically trained on smaller, task-specific datasets curated for applications like text classification, sentiment analysis, or entity extraction. These models excel in narrow, well-defined use cases. 

LLM: Trained on massive, diverse datasets, enabling them to generalize across tasks such as writing creative content, answering open-ended questions, and engaging in context-aware dialogues. This broader scope, however, demands extensive computational resources. 

2. Context Understanding 

NLP: Processes language at the sentence or phrase level, often lacking the ability to understand extended contexts. 

LLM: Utilizes attention mechanisms (e.g., transformers) to track context across paragraphs or entire documents, making responses more cohesive and context-aware. 

3. Model Architecture 

NLP: Relies on traditional techniques like bag-of-words models, N-grams, and simpler deep learning models such as recurrent neural networks (RNNs). These techniques are often effective for structured, task-specific language processing but may lack the depth and contextual understanding offered by more advanced models. 

LLM: Built on advanced transformer-based architectures like Generative Pre-trained Transformers (GPT) or Bidirectional Encoder Representations from Transformers (BERT), which allow for parallel processing and better handling of complex patterns in language. 

4. Scalability 

NLP: Lightweight and easier to deploy on limited hardware or in environments with constraints on resources. 

LLM: Requires significant computational power for both training and inference, often necessitating specialized hardware like graphical processing units (GPUs) or tensor processing units (TPUs). 

5. Output Flexibility 

NLP: Outputs are often deterministic and predefined, offering answers or actions based on fixed logic. 

LLM: Generates diverse, dynamic outputs, including creative and hypothetical scenarios, making it suitable for unstructured or exploratory tasks. 

6. Integration with Other Tools 

NLP: Easily integrated into existing systems for structured tasks, such as chatbots, search engines, and data extraction workflows. 

LLM: Requires more complex integration due to its scale and broader range of capabilities, but it can also adapt to diverse roles with fine-tuning. 

7. Performance on Low-Resource Languages 

NLP: Performance depends on the availability of datasets for the language or dialect in question. It may struggle with low-resource languages. 

LLM: Often trained on multilingual datasets, giving it a baseline capability in handling lesser-used languages, though it still exhibits variability in performance. 

8. Human Oversight and Fine-Tuning 

NLP: Requires explicit rule definitions or supervised learning processes, making human intervention critical at the design stage. 

LLM: Involves fine-tuning on specific datasets post-training but can perform multiple tasks with minimal human intervention due to its pretraining. 

9. Error Propagation 

NLP: Errors are often contained to particular components, such as a poorly trained sentiment analysis module. 

LLM: Errors can cascade, particularly when the model generates plausible but incorrect responses due to overgeneralization.

LLM and NLP in Action: Common Use Cases 

LLM Use Cases

• When used in chatbots or conversational AI, LLMs provide a more natural user experience than NLP. LLM chatbots grasp the nuances in user messages and respond accordingly, closely simulating a human conversation.
• LLMs can help with content creation based on prompts or by analyzing structured data. This is useful in scenarios where time is of the essence and there’s a need to ensure rapid content delivery, such as newsrooms or news websites.
• LLMs are useful for language translation. They deliver fast translations while maintaining contextual accuracy and readability, giving them an edge over other translation methods. 
• In software development, LLMs can support programmers by generating, reviewing, and even debugging code.
• LLMs help provide personalized learning experiences in education by analyzing students’ progress, recommending study materials, or creating custom quizzes.
• LLMs can simplify complex data integration processes by crafting data mapping suggestions, or identifying schema mismatches when consolidating data from multiple sources.
• By analyzing datasets, LLMs can automatically generate descriptive metadata tags, improving data cataloging and facilitating faster data discovery in storage or warehousing systems. 

NLP Use Cases

• NLP is useful for spam detection, social media monitoring, and customer feedback analysis. It can recognize certain keywords and analyze text structure to categorize it as spam or legitimate.
• NLP enables search engines to understand user queries and intent, improving the relevance and accuracy of search results. 
• NLP can convert speech to text and vice versa, which is useful for accessibility tools and transcription platforms. 
• Information extraction and document summarization are two areas where NLP excels. It can quickly obtain the most relevant information from different documents or summarize lengthy texts to save time. 
• Virtual assistants such as Google Assistant, Siri, and Alexa use NLP to understand human speech and respond appropriately to verbal commands.
• NLP can parse unstructured text data to detect and standardize inconsistencies, such as variations in names, dates, or addresses, ensuring data quality in data management workflows.
• NLP can interpret natural language queries and translate them into structured database queries (e.g., SQL), enabling non-technical users to interact with databases or data warehouses effectively.

LLM or NLP: Deciding Which One to Use 

The NLP vs. LLM debate isn’t new, but the fact of the matter is that one isn’t inherently better than the other. NLP and LLM are complementary technologies that work best when used together. As a duo, they bolster each other’s strengths and mitigate individual limitations. 

How to Use LLM and NLP Together 

• Preprocessing Using NLP to Improve LLM Performance 

Preprocessing cleans raw data, ensuring that it’s structured and ready for analysis by LLMs. For example, NLP can standardize formatting and remove spelling mistakes during email classification to provide consistent input for LLM-based categorization. 

• Merging Rule-Based NLP with LLM Insights 

Rule-based NLP can tackle structured or repetitive tasks, while LLMs can be deployed to address bigger, context-sensitive challenges. For instance, when used for fraud detection, NLP can identify patterns such as unusual phrasing or repeated keywords in documents, while an LLM can assess the document as a whole. 

• Fine-Tuning an LLM with Domain-Specific NLP 

NLP can give domain-specific annotations that improve LLMs’ ability to perform specialized tasks. For instance, in e-commerce, NLP can identify product categories, and the LLM can generate customized product descriptions based on the extracted attributes. 

• Post-Processing LLM Outputs with NLP 

NLP techniques can further refine or validate LLM outputs to maintain consistency with user requirements or business guidelines. For example, LLM can create first drafts for marketing copy and NLP can review them for sentiment, brand voice, and tone.

Use Cases Combining NLP and LLM

• Intelligent Document Processing (IDP): NLP extracts entities like names, dates, and amounts from structured or semi-structured documents, while LLMs refine contextual understanding to handle ambiguous or unstructured text and generate summaries.
• Customer Support Automation: NLP identifies customer intent and processes initial queries, while LLMs provide detailed, context-aware responses and escalate complex cases when needed. 
• Content Personalization: NLP analyzes user preferences and interactions, and LLMs generate personalized recommendations or dynamic content, such as tailored emails or product descriptions.
• Sentiment Analysis and Trend Prediction: NLP classifies sentiment from social media or reviews, while LLMs identify emerging trends, patterns, and implications across large datasets for actionable insights. 

Benefits of a Mixed Approach 

• Better Resource Utilization: NLP can capably handle simpler tasks for which resource-intensive LLMs can be overkill.
• Improved Accuracy: Together, NLP’s structured logic and LLMs’ contextual capabilities can deliver more accurate results.
• Cost Optimization: Using NLP for preprocessing or other small tasks can lower the costs associated with LLM deployment.
• Increased Flexibility and Scalability: Modular systems can engage NLP for foundational tasks and LLMs for more complex processing requirements, increasing scalability.

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Final Word 

There’s no better example of NLP and LLM working well together than intelligent document processing (IDP). IDP utilizes both these technologies and far outperforms traditional document processing and simple automation. 

Astera’s automated IDP solution is a cutting-edge no-code offering that changes how businesses approach document management. It rapidly extracts data from various file types, identifies and fetches pertinent information from specified fields, and makes it easy to obtain in-depth insights using natural language queries. Astera also supports LLM fine-tuning and custom LLM creation. 

Discover how to make the most of the NLP-LLM synergy to improve your document processing methods. Speak to our team today!