# Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation

arXiv: https://arxiv.org/abs/2509.01185 · HTML: https://mlsanity.com/paper/2509.01185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA](https://mlsanity.com/paper/2406.17419/) (2024)
- [LongAlign: A Recipe for Long Context Alignment of Large Language Models](https://mlsanity.com/paper/2401.18058/) (2024)
- [Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models](https://mlsanity.com/paper/2402.02244/) (2024)
- [Evaluating Language Models as Synthetic Data Generators](https://mlsanity.com/paper/2412.03679/) (2024)
- [LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization](https://mlsanity.com/paper/2502.13922/) (2025)
- [S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2310.15147/) (2023)
- [DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis](https://mlsanity.com/paper/2505.14971/) (2025)
- [Few-shot LLM Synthetic Data with Distribution Matching](https://mlsanity.com/paper/2502.08661/) (2025)
- [WildLong: Synthesizing Realistic Long-Context Instruction Data at Scale](https://mlsanity.com/paper/2502.16684/) (2025)
- [LongAttn: Selecting Long-context Training Data via Token-level Attention](https://mlsanity.com/paper/2502.16860/) (2025)
- [Red-Teaming for Inducing Societal Bias in Large Language Models](https://mlsanity.com/paper/2405.04756/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning to Learn from Context: Synthetic Training from Perturbed Public Documents](https://mlsanity.com/paper/2609.33642/) (2026)

## Abstract (arXiv)

The ability of large language models (LLMs) to process and reason over long textual inputs is critical for a wide range of real-world applications. However, progress in this area is significantly constrained by the absence of high-quality, diverse, and verifiable long-context datasets suitable for both training and evaluation. This work introduces a modular, extensible framework for synthetic long-context data generation via prompt-based interaction with LLMs. The framework supports multiple training and alignment objectives, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). It encompasses four core generation paradigms: multi-turn conversational dialogues, document-grounded input-output pairs, verifiable instruction-response tasks, and long-context reasoning examples. Through templated prompting, a model-agnostic architecture, and metadata-enriched outputs, the proposed approach facilitates scalable, controllable, and purpose-aligned dataset creation for advancing long-context capabilities in LLMs.