# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 2 Related Work  
Text simplification aims to rewrite the original text into a simpler form while preserving its meaning. One simple way is to replace complex words in the sentences with simpler synonymous words; this process is referred to as lexical simplification. A more advanced method is to reduce the complexity of sentence structures, a process known as syntactic simplification. Existing approaches in these two directions can be categorized into three types: (i) rule-based methods, (ii) data-driven methods, and (iii) generative approaches. In this direction, most existing studies [5; 8; 9; 10; 11] generally follow the following sequence of steps: (i) identify complex words, (ii) generate a set of candidate substitutions, (iii) select the most contextually appropriate alternatives, and (iv) rank the candidate substitutions according to their simplicity. Early rule-based lexical simplification (LS) system [12] is proposed to simplify English newspaper texts for aphasic readers by combining syntactic analysis and simplification modules. It used linguistic analysis to generate synonym lists from WordNet [8], ranked by frequency from the Oxford Psycholinguistic Database, selecting the most common synonyms for output. Data-driven lexical simplification approaches use the large parallel datasets of complex and simple texts and employ machine learning techniques to learn text simplification rules. In this direction, [PERSON] and [PERSON] [10] conducted an empirical analysis of lexical simplification in Spanish using a parallel corpus of original and manually simplified texts. Their study identified lexical substitution as the most frequent operation and proposed a taxonomy including the definition insertion and simplification of named entities and numerical expressions. The authors highlighted frequency and word length as key features for synonym selection, while emphasizing the importance of word sense disambiguation for handling polysemy. [PERSON] [11] investigates techniques for automatically identifying complex words, a critical yet often under-addressed component of lexical simplification. Using a corpus derived from Simple Wikipedia edit histories, the study compares methods that include full simplification, frequency thresholding, and a supervised classification approach based on support vector machines. The results indicate that, while machine learning slightly improves precision, it suffers from a substantial loss in recall. The work emphasizes the trade-offs involved in CW identification and highlights its foundational role in downstream simplification tasks. For syntactic simplification, early approaches were also based on the hand-crafted rules [13], they framed the task as a two-step process of analysis and transformation, using handcrafted rules to split complex structures like relative clauses into simpler sentences. [PERSON] [14] introduced a framework for text simplification using transformation rules applied to typed dependency structures. The study compared different generation strategies and highlighted the trade-offs between preserving original sentence structure and relying on full-surface realization, emphasizing robustness to parsing errors as a key factor in simplification quality. [PERSON] and [PERSON] [15] propose a data-driven approach to sentence simplification using quasi-synchronous grammar and integer linear programming. Their model captures structural mismatches and complex rewrite operations, selecting optimal simplifications from a space of candidate rewrites. Experimental results show that their method improves readability while preserving grammaticality and meaning, without relying on handcrafted rules. Recent work for text simplification were employed sequence to sequence techniques and the generative approaches based on pre-trained large language models. [PERSON] and [PERSON] [16] introduce a deep reinforcement learning framework for sentence simplification, combining an encoder-decoder architecture with a reward function that promotes fluency, simplicity, and meaning preservation.