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References (50)

[1]
Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
2025Sukjun Hwang, Brandon Wang et al.
[2]
Max–Min semantic chunking of documents for RAG application
2025Csaba Kiss, Marcell Nagy et al.
[3]
Document Segmentation Matters for Retrieval-Augmented Generation
2025Zhitong Wang, Cheng Gao et al.
[4]
Random Tree Model of Meaningful Memory
2024Weishun Zhong, Tankut Can et al.
[5]
Information rate of meaningful communication
2024Doron Sivan, M. Tsodyks
[6]
Is Semantic Chunking Worth the Computational Cost?
2024Renyi Qu, Ruixuan Tu et al.
[7]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[8]
LumberChunker: Long-Form Narrative Document Segmentation
2024André V. Duarte, J. Marques et al.
[9]
Algorithmic progress in language models
2024Anson Ho, T. Besiroglu et al.
[10]
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
2024Parth Sarthi, Salman Abdullah et al.
[11]
Large-scale study of human memory for meaningful narratives
2023Antonios Georgiou, Tankut Can et al.
[12]
Lost in the Middle: How Language Models Use Long Contexts
2023Nelson F. Liu, Kevin Lin et al.
[13]
LLMZip: Lossless Text Compression using Large Language Models
2023Chandra Shekhara Kaushik Valmeekam, K. Narayanan et al.
[14]
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
2023Ronen Eldan, Yuan-Fang Li
[15]
Context Dependent Semantic Parsing: A Survey
2020Zhuang Li, Lizhen Qu et al.
[16]
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
2020Patrick Lewis, Ethan Perez et al.
[17]
Scaling Laws for Neural Language Models
2020J. Kaplan, Sam McCandlish et al.
[18]
Is Neural Language Model Perplexity Related to Readability?
2020Alessio Miaschi, Chiara Alzetta et al.
[19]
Cross Entropy of Neural Language Models at Infinity—A New Bound of the Entropy Rate
2018Shuntaro Takahashi, Kumiko Tanaka-Ishii
[20]
Predicting While Comprehending Language: A Theory and Review
2018M. Pickering, C. Gambi

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"Develop a semantic chunking tool to quantitatively analyze the structure and redundancy of natural language texts."

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5

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0/4 signals

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