Is NLP dead now that LLMs are here?
The success of large language models like GPT-4.5, Claude, Gemini, and others has overshadowed other NLP approaches.
Media and tech discussions often focus on LLMs because they are groundbreaking and produce impressive results. LLMs excel at generating human-like text, which is highly visible and marketable.
But is NLP dead?
No, NLP is far from dead.
In fact, it is thriving and evolving alongside the advent of LLMs. While LLMs are transforming many aspects of the field, they do not render traditional NLP obsolete.
NLP vs LLMs
Natural Language Processing (NLP) is a broad field of study within artificial intelligence (AI) focused on the interaction between computers and human (natural) languages. Its goal is to enable machines to understand, interpret, generate, and respond to human language in a meaningful way.
Large Language Models (LLMs) are a subset of NLP models characterized by their large size and ability to generate coherent, contextually appropriate text. They are based on transformer architectures (e.g., GPT, BERT) and trained on vast amounts of text data.
NLP poses the problems, LLMs solve them
You can think of LLMs as one type of NLP model.
They are highly capable, but they exist within the larger NLP ecosystem. NLP encompasses all techniques and methodologies for processing language, including non-neural approaches and smaller, specialized models.
Many NLP tasks don't require LLMs. For simpler, lightweight applications (e.g., spell checkers, keyword extraction, or chatbots with predefined responses), smaller and faster models or rule-based methods may be more practical.
Also, LLMs are computationally expensive. They require significant memory, processing power, and energy to train and use. Many organizations prefer smaller NLP models for cost and energy efficiency, especially in real-time or embedded applications.
Smaller models excel in specific use cases. Tailored models that are fine-tuned for a particular domain or task often outperform LLMs in accuracy, speed, or both.
Regulatory concerns favor transparency. Some industries demand traceable AI systems, where simpler NLP approaches may be better suited.
So what else is there?
In 2025, traditional NLP techniques continue to play a vital role alongside LLMs, enhancing performance, efficiency, and reliability across various applications.
Processes like tokenization, stemming, and lemmatization break down text into manageable units and normalize word forms, providing cleaner inputs for LLMs and improving their understanding of context.
For tasks requiring high precision, such as (extracting specific data formats)[/blog/what-is-unstructured-data-extraction] (e.g., dates, phone numbers), rule-based methods provide reliable results that can be integrated with LLM outputs for enhanced accuracy.
Integrating traditional NLP with LLMs leads to systems that leverage the strengths of both approaches. You can use NLP for tasks like entity extraction and data normalization, then employ LLMs to generate insights or conversational responses based on that structured data.
Combining LLMs with knowledge graphs (KGs) offers structured, interpretable information that enhances the reasoning and factual accuracy of language models.
There are also problems that are so well solved already with traditional NLP techniques that there is no need to use LLMs.
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Support Vector Machines (SVMs) efficiently classify emails as spam or not, a task where LLMs might be unnecessarily complex.
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Naive Bayes classifiers can quickly and accurately determine sentiment in text, especially when trained on domain-specific data.
Traditional NLP techniques are not only alive but are essential companions to LLMs in 2025. By combining the precision and efficiency of established methods with the generative power of LLMs, we achieve more robust, accurate, and context-aware language processing systems.
