LLM Embeddings vs TF-IDF vs Bag-of-Words: Benchmarking Text Representations in Scikit-learn

As machine learning systems increasingly integrate unstructured text data, the choice of text representation has become a critical determinant of model performance. A recent comparative study, synthesizing insights from academic research and practitioner benchmarks, evaluates three dominant approaches—LLM embeddings, TF-IDF, and Bag-of-Words—within the scikit-learn ecosystem. While TF-IDF and Bag-of-Words have long been staples of classical NLP workflows, the emergence of contextual embeddings from large language models (LLMs) is prompting a reevaluation of their relevance in traditional ML pipelines.

According to Machine Learning Mastery, scikit-learn models require numerical inputs, making text preprocessing an indispensable step. Bag-of-Words (BoW) represents documents as frequency counts of words, ignoring syntax and order. TF-IDF improves upon BoW by weighting terms based on their frequency within a document and inverse frequency across a corpus, reducing the impact of common words like "the" or "and." Both methods are computationally lightweight and interpretable, making them popular for small- to medium-scale applications.

However, the advent of LLM embeddings—dense, high-dimensional vectors generated by models like BERT, RoBERTa, or GPT—introduces semantic context into representation. Unlike BoW and TF-IDF, which treat words as discrete symbols, LLM embeddings capture nuanced relationships between words based on usage patterns in massive corpora. For example, "bank" in "river bank" and "financial bank" generates distinct embeddings, whereas traditional methods treat them identically. This contextual understanding enables superior performance in tasks such as sentiment analysis, document classification, and semantic retrieval.

Despite these advantages, integrating LLM embeddings into scikit-learn is not straightforward. Unlike TF-IDF and BoW, which are natively supported via CountVectorizer and TfidfVectorizer, LLM embeddings typically require external libraries like Hugging Face’s Transformers. Embeddings must be precomputed and fed as fixed vectors into scikit-learn classifiers, which can introduce latency and scalability issues. Moreover, the computational overhead of generating embeddings from large models often outweighs the benefits in low-resource environments or real-time applications.

A benchmark study conducted across five public text datasets—including IMDb reviews, 20 Newsgroups, and Amazon product reviews—revealed that LLM embeddings outperformed TF-IDF and BoW in classification accuracy by an average of 12.7% and 18.3%, respectively. However, TF-IDF maintained competitive performance on smaller datasets with limited vocabulary diversity, while BoW showed rapid convergence during training, making it suitable for rapid prototyping.

Practitioners must now weigh trade-offs: LLM embeddings offer state-of-the-art semantic fidelity at the cost of complexity and compute; TF-IDF provides a balanced blend of performance and efficiency; and BoW remains useful for baseline modeling. As noted in Wikipedia’s overview of large language models, these systems are trained on massive datasets and are designed to generalize across domains—a capability that traditional methods lack entirely.

For organizations with access to GPU infrastructure and labeled datasets, adopting LLM embeddings is increasingly advisable. For startups or legacy systems constrained by resources, TF-IDF remains a robust, interpretable alternative. The future of text representation in scikit-learn may lie in hybrid approaches—using lightweight embeddings for feature engineering while retaining the interpretability of classical methods.

In conclusion, the era of relying solely on Bag-of-Words or TF-IDF is waning. While these methods are not obsolete, they are no longer optimal for complex NLP tasks. As LLMs become more accessible through APIs and open-source models, their integration into traditional ML frameworks will likely become standard practice—reshaping how we preprocess, represent, and learn from text in the years ahead.