mlsanityData to 14 Oct 2026

A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences

arXiv 2506.11681 ↗First posted 13 Jun 2025cs.CLcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

This paper addresses the challenge of transforming complex sentences into sequences of logical, simplified sentences while preserving semantic and logical integrity with the help of Large Language Models. We propose a hybrid approach that combines advanced prompting with multi-agent architectures to enhance the sentence simplification process. Experimental results show that our approach was able to successfully simplify 70% of the complex sentences written for video game design application. In comparison, a single-agent approach attained a 48% success rate on the same task.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 15 of its 24 references that are in the map. First posted on arXiv 13 Jun 2025.

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15 of 24 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems16 Feb
  2. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMs10 Jan
  3. 2024LLM Multi-Agent Systems: Challenges and Open Problems5 Feb
  4. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges21 Jan

Other references · 11

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022A Survey on In-context Learningcited by 721
  3. 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 207
  4. 2022Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Orderingcited by 161
  5. 2024The Prompt Report: A Systematic Survey of Prompt Engineering Techniquescited by 121
  6. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 117
  7. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  8. 2023Meta-in-context learning in large language modelscited by 59
  9. 2023Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4cited by 48
  10. 2022On the Ethical Considerations of Text Simplificationcited by 20
  11. 2023Sentence Simplification via Large Language Modelscited by 15

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