Investigating Large Language Models' Linguistic Abilities for Text Preprocessing

• URL: http://arxiv.org/abs/2510.11482v1
• Date: Mon, 13 Oct 2025 14:53:44 GMT
• Title: Investigating Large Language Models' Linguistic Abilities for Text Preprocessing
• Authors: Marco Braga, Gian Carlo Milanese, Gabriella Pasi,
• Abstract summary: This paper investigates the idea of using Large Language Models (LLMs) to perform various preprocessing tasks.<n>We compare LLM-based preprocessing to traditional algorithms across multiple text classification tasks in six European languages.<n>Our analysis indicates that LLMs are capable of replicating traditional stopword removal, lemmatization, and stemming methods with accuracies reaching 97%, 82%, and 74%, respectively.
• Score: 7.855724133071134
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Text preprocessing is a fundamental component of Natural Language Processing, involving techniques such as stopword removal, stemming, and lemmatization to prepare text as input for further processing and analysis. Despite the context-dependent nature of the above techniques, traditional methods usually ignore contextual information. In this paper, we investigate the idea of using Large Language Models (LLMs) to perform various preprocessing tasks, due to their ability to take context into account without requiring extensive language-specific annotated resources. Through a comprehensive evaluation on web-sourced data, we compare LLM-based preprocessing (specifically stopword removal, lemmatization and stemming) to traditional algorithms across multiple text classification tasks in six European languages. Our analysis indicates that LLMs are capable of replicating traditional stopword removal, lemmatization, and stemming methods with accuracies reaching 97%, 82%, and 74%, respectively. Additionally, we show that ML algorithms trained on texts preprocessed by LLMs achieve an improvement of up to 6% with respect to the $F_1$ measure compared to traditional techniques. Our code, prompts, and results are publicly available at https://github.com/GianCarloMilanese/llm_pipeline_wi-iat.

Related papers

• Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data [18.87770758217633]
  Lemmatization is the task of transforming all words in a given text to their dictionary forms.<n>There is no prior evidence of how effective large language models are in the contextual lemmatization task.<n>This paper empirically investigates the capacity of the latest generation of LLMs to perform in-context lemmatization.
  arXiv  Detail & Related papers  (2025-10-08T18:34:00Z)
• Semantic Outlier Removal with Embedding Models and LLMs [0.45080838507508303]
  We introduce SORE (Semantic Outlier Removal), a cost-effective, transparent method to identify and excise unwanted text segments.<n>SORE achieves near-LLM extraction precision at a fraction of the cost.<n>Our system is currently deployed in production, processing millions of documents daily across multiple languages.
  arXiv  Detail & Related papers  (2025-06-19T23:06:12Z)
• Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective [40.29094043868067]
  We present the first systematic study of the diffusion language embedding model, which outperforms the LLM-based embedding model by 20% on long-document retrieval.<n>Our analysis verifies that bidirectional attention is crucial for encoding global context in long and complex text.
  arXiv  Detail & Related papers  (2025-05-21T02:59:14Z)
• END: Early Noise Dropping for Efficient and Effective Context Denoising [60.24648712022382]
  Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks.<n>They are often distracted by irrelevant or noisy context in input sequences that degrades output quality.<n>We introduce Early Noise Dropping (textscEND), a novel approach to mitigate this issue without requiring fine-tuning the LLMs.
  arXiv  Detail & Related papers  (2025-02-26T08:07:17Z)
• PICASO: Permutation-Invariant Context Composition with State Space Models [98.91198288025117]
  State Space Models (SSMs) offer a promising solution by allowing a database of contexts to be mapped onto fixed-dimensional states.<n>We propose a simple mathematical relation derived from SSM dynamics to compose multiple states into one that efficiently approximates the effect of concatenating raw context tokens.<n>We evaluate our resulting method on WikiText and MSMARCO in both zero-shot and fine-tuned settings, and show that we can match the strongest performing baseline while enjoying on average 5.4x speedup.
  arXiv  Detail & Related papers  (2025-02-24T19:48:00Z)
• Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding [71.01099784480597]
  Large language models (LLMs) excel at a range of tasks through in-context learning (ICL)<n>We introduce In-Context Contrastive Decoding (ICCD), a novel method that emphasizes input-label mapping.
  arXiv  Detail & Related papers  (2025-02-19T14:04:46Z)
• Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu [53.437954702561065]
  In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT.<n>This study systematically investigates how each type of resource, e.g., dictionary, grammar book, and retrieved parallel examples, affect the translation performance.<n>Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help.
  arXiv  Detail & Related papers  (2025-02-17T14:53:49Z)
• Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning [53.57895922042783]
  Large Language Models (LLMs) excel at reasoning and planning when trained on chainof-thought (CoT) data.<n>We propose a hybrid representation of the reasoning process, where we partially abstract away the initial reasoning steps using latent discrete tokens.
  arXiv  Detail & Related papers  (2025-02-05T15:33:00Z)
• A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models [0.8899670429041453]
  We show that generative large language models (LLMs) can solve NLP tasks with very high quality without the need for extensive data. Based on a novel prompting strategy, we show that LLMs are able to outperform state-of-the-art machine learning approaches.
  arXiv  Detail & Related papers  (2024-07-26T06:39:35Z)
• Adaptable and Reliable Text Classification using Large Language Models [7.962669028039958]
  This paper introduces an adaptable and reliable text classification paradigm, which leverages Large Language Models (LLMs)<n>We evaluated the performance of several LLMs, machine learning algorithms, and neural network-based architectures on four diverse datasets.<n>It is shown that the system's performance can be further enhanced through few-shot or fine-tuning strategies.
  arXiv  Detail & Related papers  (2024-05-17T04:05:05Z)
• Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code? [51.29970742152668]
  We highlight relying on accuracy-based measurements may lead to an overestimation of models' capabilities. To address these issues, we introduce a technique called SyntaxEval in Syntactic Capabilities.
  arXiv  Detail & Related papers  (2024-01-03T02:44:02Z)
• The Ups and Downs of Large Language Model Inference with Vocabulary Trimming by Language Heuristics [74.99898531299148]
  This research examines vocabulary trimming (VT) inspired by restricting embedding entries to the language of interest to bolster time and memory efficiency. We apply two languages to trim the full vocabulary - Unicode-based script filtering and corpus-based selection - to different language families and sizes. It is found that VT reduces the memory usage of small models by nearly 50% and has an upper bound of 25% improvement in generation speed.
  arXiv  Detail & Related papers  (2023-11-16T09:35:50Z)
• Exploring Human-Like Translation Strategy with Large Language Models [93.49333173279508]
  Large language models (LLMs) have demonstrated impressive capabilities in general scenarios. This work proposes the MAPS framework, which stands for Multi-Aspect Prompting and Selection. We employ a selection mechanism based on quality estimation to filter out noisy and unhelpful knowledge.
  arXiv  Detail & Related papers  (2023-05-06T19:03:12Z)
• A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance [0.0]
  We improve the conventional methodology of automatic readability assessment by incorporating the Word Mover's Distance (WMD) of ranked texts. Results of our experiments on three multilingual datasets in Filipino, German, and English show that the post-processing technique outperforms previous vanilla and ranking-based models.
  arXiv  Detail & Related papers  (2021-03-12T13:51:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.