Let's Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification

• URL: http://arxiv.org/abs/2602.07499v1
• Date: Sat, 07 Feb 2026 11:21:17 GMT
• Title: Let's Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification
• Abstract summary: Large language models demonstrate limited capability in proficiency-controlled sentence simplification.<n>We propose a framework that decomposes complex simplifications into manageable steps.<n> Evaluation on five languages shows our approach improves simplification effectiveness while reducing computational steps by 22-42%.
• Score: 8.985694067999864
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models demonstrate limited capability in proficiency-controlled sentence simplification, particularly when simplifying across large readability levels. We propose a framework that decomposes complex simplifications into manageable steps through dynamic path planning, semantic-aware exemplar selection, and chain-of-thought generation with conversation history for coherent reasoning. Evaluation on five languages across two benchmarks shows our approach improves simplification effectiveness while reducing computational steps by 22-42%. Human evaluation confirms the fundamental trade-off between simplification effectiveness and meaning preservation. Notably, even human annotators struggle to agree on semantic preservation judgments, highlighting the inherent complexity of this task. Our work shows that while step-by-step simplification improves control, preserving semantic fidelity during extensive simplification remains an open challenge.

Related papers

• Policy-based Sentence Simplification: Replacing Parallel Corpora with LLM-as-a-Judge [14.557044002544586]
  Sentence simplification aims to modify a sentence to make it easier to read and understand while preserving the meaning.<n>Different applications require distinct simplification policies, such as replacing only complex words at the lexical level or rewriting the entire sentence.<n>We introduce a simple yet powerful approach that leverages Large Language Model-as-a-Judge (LLM-as-a-Judge) to automatically construct policy-aligned training data.
  arXiv  Detail & Related papers  (2025-12-06T00:29:49Z)
• A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences [0.0]
  We propose a hybrid approach that combines advanced prompting with multi-agent architectures to enhance the sentence simplification process.<n> Experimental results show that our approach was able to successfully simplify 70% of the complex sentences written for video game design application.
  arXiv  Detail & Related papers  (2025-06-13T11:19:27Z)
• Multi-Modal Self-Supervised Semantic Communication [52.76990720898666]
  We propose a multi-modal semantic communication system that leverages multi-modal self-supervised learning to enhance task-agnostic feature extraction.<n>The proposed approach effectively captures both modality-invariant and modality-specific features while minimizing training-related communication overhead.<n>The findings underscore the advantages of multi-modal self-supervised learning in semantic communication, paving the way for more efficient and scalable edge inference systems.
  arXiv  Detail & Related papers  (2025-03-18T06:13:02Z)
• Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models [56.37421741507468]
  Chain-of-Thought (CoT) reasoning has significantly enhanced the performance of large language models (LLMs)<n>We propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
  arXiv  Detail & Related papers  (2025-02-18T20:04:51Z)
• Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition [11.700462697630696]
  This study aims to facilitate English as a Second Language learners' language acquisition by simplification.<n>We propose simplifying complex sentences to appropriate levels for learners while also increasing vocabulary coverage of the target level in the simplifications.<n>Our method employs token-level and sentence-level rewards, and iteratively trains the model on its self-generated outputs to guide the model to search for simplification hypotheses that satisfy the target attributes.
  arXiv  Detail & Related papers  (2025-02-17T05:32:56Z)
• Enhancing LLM Character-Level Manipulation via Divide and Conquer [74.55804812450164]
  Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks.<n>They exhibit notable weaknesses in character-level string manipulation, struggling with fundamental operations such as character deletion, insertion, and substitution.<n>We propose Character-Level Manipulation via Divide and Conquer, a novel approach designed to bridge the gap between token-level processing and character-level manipulation.
  arXiv  Detail & Related papers  (2025-02-12T07:37:39Z)
• Progressive Document-level Text Simplification via Large Language Models [19.57555397986868]
  Long document-level simplification (DS) is still relatively unexplored.<n>We propose a progressive simplification method (ProgDS) by hierarchically decomposing the task.
  arXiv  Detail & Related papers  (2025-01-07T15:14:37Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• Re-Reading Improves Reasoning in Large Language Models [87.46256176508376]
  We introduce a simple, yet general and effective prompting method, Re2, to enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs) Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. We evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.
  arXiv  Detail & Related papers  (2023-09-12T14:36:23Z)
• Unsupervised Sentence Simplification via Dependency Parsing [4.337513096197002]
  We propose a simple yet novel unsupervised sentence simplification system. It harnesses parsing structures together with sentence embeddings to produce linguistically effective simplifications. We establish the unsupervised state-of-the-art at 39.13 SARI on TurkCorpus set and perform competitively against supervised baselines on various quality metrics.
  arXiv  Detail & Related papers  (2022-06-10T07:55:25Z)
• Learning to Selectively Learn for Weakly-supervised Paraphrase Generation [81.65399115750054]
  We propose a novel approach to generate high-quality paraphrases with weak supervision data. Specifically, we tackle the weakly-supervised paraphrase generation problem by:. obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.
  arXiv  Detail & Related papers  (2021-09-25T23:31:13Z)
• Controllable Text Simplification with Explicit Paraphrasing [88.02804405275785]
  Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. We propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles.
  arXiv  Detail & Related papers  (2020-10-21T13:44:40Z)

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.