Learning to Learn from Context: Synthetic Training from Perturbed Public Documents
Real-world tasks often require large language models (LLMs) to learn from complex task-specific context rather than pretrained parametric knowledge. This capability remains a weakness of LLMs, while human annotation for such task contexts is expensive and difficult to scale. Public high-quality documents are an abundant alternative, but much of the public web has already been consumed during pretraining: training on such documents naively would reward memorization rather than context learning. In this work, we attempt to make use of high-quality public documents with small perturbations and empirically find that LLMs can successfully generate context-dependent reasoning traces and answers...
| Authors | Haoyi Wu, Yang Xiao, Yusong Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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