Lexical and Syntactic Simplification

Lexical simplification focuses on replacing complex words with simpler, more common alternatives without changing the meaning ​(ar5iv.org). For example, a term like “endeavor” might be replaced with “try” to enhance readability. Traditional approaches treat lexical simplification as a pipeline of sub-tasks: identify difficult words, generate simpler candidate synonyms, then rank those candidates in context (ar5iv.org). Recent research has improved this process by using large pretrained models – LSBert (2020) used BERT to consider the wider sentence context when suggesting and ranking substitutions, achieving substantially higher accuracy (nearly +30 points on benchmarks) than earlier methods​ (ar5iv.org). Newer studies even show that large language models (LLMs) can simplify whole sentences directly via prompting, often outperforming the multi-step pipeline by handling lexical replacements and grammar adjustments in one go​ (arxiv.org)(arxiv.org).

Syntactic simplification aims to reduce grammatical complexity and sentence length. This involves transforming complex constructions (e.g. converting passive voice to active, or splitting long sentences at conjunctions and relative clauses) into simpler sentence structures​ (arxiv.org). Such transformations often require inserting referents or minor edits to preserve coherence – for instance, breaking a sentence with a subordinate clause into two sentences may require adding a pronoun or connective for the second sentence​ (arxiv.org). Syntactic simplification is frequently combined with lexical simplification in text simplification systems, including modern sequence-to-sequence models that learn to paraphrase complex sentences into simpler ones. Research in 2021 introduced large-scale training data for sentence splitting (the BiSECT corpus of 1 million sentence pairs) and new models that can learn where to split and rephrase; this led to improved performance over previous benchmarks in both automatic metrics and human evaluation​ (aclanthology.org)(aclanthology.org). Overall, recent work highlights that combining straightforward vocabulary with simpler syntax can significantly improve text accessibility and also make it easier for NLP models to process text efficiently. Open-source tools like LightLS exemplify these ideas – LightLS uses word frequency lists and embedding similarity to automatically substitute rare words with more common ones, provided the replacement is semantically close in context​ (github.com)(github.com). This kind of rule-informed lexical simplification can be a fast, language-agnostic preprocessing step to reduce complexity before deeper neural processing.

Controlled Natural Language (CNL) Processing

Controlled Natural Languages are purposely simplified versions of natural languages that restrict vocabulary and grammar rules to reduce ambiguity and complexity​ (oneword.de). The goal is to produce text that is easier for machines to parse while still readable for humans. CNLs often enforce one interpretation per sentence structure or word, which improves machine readability (unambiguous parsing or translation) and consistency in tokenization. A classic example from industry is Simplified Technical English (STE), used in aerospace and engineering documentation. STE limits the lexicon to a standardized subset of English and prescribes strict grammatical rules, so each approved word has one clear meaning and complex sentences are broken into simpler ones​ (oneword.de)(oneword.de). This yields documentation that is easier to understand for non-native readers and also easier to translate or process automatically. The use of CNL can thus “standardize” text input for NLP systems. For instance, enforcing CNL rules in logistics communication has been proposed to let human operators and information systems use the same messages – the text is controlled enough that computers can accurately parse it, yet still natural for humans to read​ (aclanthology.org)(aclanthology.org). Academic and industry research is exploring CNL as a bridge to structured representations. One study (Lehmann et al. 2023) showed that using a controlled English as the intermediate query language for a knowledge graph Q&A system allowed an LLM to parse questions into a formal representation (SPARQL) more reliably and with far less training data than directly learning the formal query syntax​ (amazon.science)(amazon.science). In summary, CNL processing uses rule-based simplification to ensure texts adhere to a limited form of language, which improves downstream NLP by making tokenization and parsing more predictable. There are even tools and standards (e.g. the ASD-STE100 specification for STE and authoring software supporting it) that implement these approaches in industry documentation workflows.

Preprocessing Techniques for LLM Efficiency

Because large language models incur cost and latency proportional to input token length, there is growing focus on preprocessing text to minimize token count while preserving meaning. Recent industry best practices emphasize concise wording and removal of redundancies in prompts – essentially prompt engineering to say the same thing in fewer tokens​ (blog.premai.io). For example, replacing a phrase like “at this point in time” with “now” or omitting extraneous details can significantly shorten input length. Beyond manual conciseness, researchers and developers are creating automated text rewriting tools to compress prompts. One such implementation is CompressGPT, which uses an LLM to rewrite a given prompt into a shorter form that is semantically equivalent, yielding around a 70% reduction in token usage in tests​ (musings.yasyf.com). CompressGPT works by identifying which parts of the prompt can be safely abbreviated and then verifying that the compressed version retains the original intent​ (musings.yasyf.com). These kinds of preprocessing pipelines effectively trade a bit of upfront computation for a leaner input to the main model, saving cost overall. Notably, a recent theoretical study proved that finding the absolutely optimal tokenization (i.e. an encoding that minimizes tokens without losing any meaning) is an NP-Complete problem​ (mikeyoung44.hashnode.dev). In other words, perfectly compressing text is computationally intractable for long inputs, so practical solutions rely on heuristics and approximations​ (mikeyoung44.hashnode.dev)(mikeyoung44.hashnode.dev). Current tokenizers (like Byte Pair Encoding and WordPiece) already aim to make tokens efficient, but these operate at the subword level. The new line of research looks at higher-level rephrasing – essentially performing a controlled abstraction or compression of the input language itself. Open-source libraries and guides have started incorporating these strategies (for instance, Hugging Face’s tiktoken and prompt optimization recipes), showing that even simple preprocessing like removing unnecessary whitespace, lowercasing consistently, or swapping in shorter synonyms can measurably reduce token counts. In summary, the state-of-the-art in LLM preprocessing combines clever prompt design with algorithmic compression techniques to maximize meaning-per-token, enabling more efficient model usage​ (blog.premai.io).

Sentence Segmentation and Normalization

Breaking down complex sentences into shorter, well-formed sentences can greatly aid both human understanding and machine processing. Research in sentence segmentation – often called the “split and rephrase” task – has shown that many NLP tasks benefit from simpler sentence inputs​ (arxiv.org). By segmenting long, convoluted sentences, we reduce the cognitive load on an LLM or parser, since each segment expresses a single idea more clearly. Recent studies have produced significant advancements in this area. For example, Ponce et al. (2023) demonstrated that fine-tuned large language models dramatically improve the quality of split-and-rephrase, outperforming prior systems by a wide margin on benchmark datasets​ (arxiv.org). Their approach preserved the original meaning while generating multiple shorter sentences, verified through both automatic metrics and human evaluation​ (arxiv.org)(arxiv.org). This indicates that modern models can learn to split sentences in an intelligent way, inserting the necessary context (e.g. replacing a dropped pronoun or adjusting verb tense) so that the resulting standalone sentences remain coherent. Large-scale resources like the WikiSplit corpus and the BiSECT dataset have also fueled progress by providing training examples of complex-to-simple sentence splits, enabling robust supervised models​ (aclanthology.org)(aclanthology.org). In practice, sentence segmentation is often a first step in text preprocessing pipelines (e.g. using tools like spaCy or CoreNLP to detect sentence boundaries), and these research advances are making that step more accurate and beneficial for downstream understanding.

Text normalization is another important preprocessing step, involving converting text to a standardized, clean form. Normalization can include expanding abbreviations and numerals into words (for example, “Dr. Smith won 3 awards” → “Doctor Smith won three awards”), standardizing date/time formats, fixing casing and punctuation, or removing noise like HTML tags. The aim is to present the language model with a consistent input format, reducing variability that the model has to handle. Traditionally, normalization for things like speech recognition or text-to-speech was done with hand-written rules and lexicons, but recent research applies LLMs to this task with impressive results. NVIDIA researchers in 2024 showed that a GPT-based approach to text normalization, using prompt-based few-shot learning with some linguistic hints, cut error rates by ~40% compared to a production rule-based system​ (research.nvidia.com)research.nvidia.com). This indicates that LLMs can generalize and correctly normalize even “non-standard” words or phrases (like slang, proper nouns, or rare formats) better than rigid pipelines, likely because the model has seen many variations during pretraining. Likewise, Meta AI has explored neural text normalization for speech (treating it as a seq-to-seq problem of mapping an spoken-form transcript to written-form text) with success​ (ai.meta.com). For industry applications, improved normalization means fewer downstream errors – e.g. a normalized input will ensure that a date like "02/03/21" is interpreted unambiguously as February 3, 2021 (or March 2, depending on convention) according to the chosen standard, rather than leaving room for confusion. Many NLP libraries include basic normalizers (for lowercasing, stemming, removing special characters), but now we also see open-source projects using learned models for advanced normalization (especially in domains like biomedical text normalization​ (medrxiv.org)). Together, sentence segmentation and normalization can be seen as text simplification at the structural and token levels. By splitting sentences and cleaning/standardizing the text, we create input that allows language models to focus on the meaning, leading to more efficient and accurate processing. Each of these steps has active research and tooling support in recent years, and they often serve as fundamental building blocks in real-world NLP pipelines.