A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only local thoughts, and leav...
arXiv:2601. 22146v2 Announce Type: replace-cross Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised "predict the next word" objective on a vast amount of unstructured text data.
By Ajay Patel, Colin Raffel, Chris Callison-Burch
OARelatedWork is a large‑scale dataset for generating entire related‑work sections from open‑access papers, comprising 94,450 papers across multiple domains. It includes full texts of cited works and is designed so every source paper is available in full, enabling controlled evaluation of full‑text related‑work generation. The paper benchmarks various models, shows that large language models struggle with massive full‑text contexts, and introduces a statement‑level evaluation framework to better assess long‑form structured outputs.
By Martin Docekal, Martin Fajcik, Pavel Smrz
arXiv:2503.06868v2 Announce Type: replace-cross
Abstract: Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored....
By Junhao Zhang, Richong Zhang, Fanshuang Kong, Ziyang Miao, Yanhan Ye, Yaowei Zheng
arXiv:2607.28109v2 Announce Type: replace
Abstract: Synthetic textbook data has improved language model pre-training, but prior work largely treats the benefit as a property of generated content or l...
By Jiawen Tao, Miao Peng, Yaoming Li, Xiaokun Yuan, Mengzhou Wu, Wenhan Yu, Guoan Wang, Nuo Chen, Tong Yang, Maxm Pan
The paper introduces VOLM, a framework that quantifies how much original value a human adds to a document beyond what a language model could generate from a task description alone. Unlike existing tools that focus on stylistic detection, VOLM extracts content at varying granularities, reconstructs it with an LLM, and compares these reconstructions to those derived from the task description. Evaluations across news articles, ICLR peer reviews, and argumentative essays show that VOLM can distinguish human-authored texts from LLM-generated ones while remaining robust to content-preserving transformations.
By Vibhhu Sharma, Thorsten Joachims, Sarah Dean