# Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's Nest

arXiv: https://arxiv.org/abs/2502.11275 · HTML: https://mlsanity.com/paper/2502.11275/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2025.

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

- [A Tale of LLMs and Induced Small Proxies: Scalable Small Language Models for Knowledge Mining](https://mlsanity.com/paper/2510.01427/) (2025)

## Abstract (arXiv)

Massive high-quality data, both pre-training raw texts and post-training annotations, have been carefully prepared to incubate advanced large language models (LLMs). In contrast, for information extraction (IE), pre-training data, such as BIO-tagged sequences, are hard to scale up. We show that IE models can act as free riders on LLM resources by reframing next-token prediction into extraction for tokens already present in the context. Specifically, our proposed next tokens extraction (NTE) paradigm learns a versatile IE model, Cuckoo, with 102.6M extractive data converted from LLM's pre-training and post-training data. Under the few-shot setting, Cuckoo adapts effectively to traditional and complex instruction-following IE with better performance than existing pre-trained IE models. As a free rider, Cuckoo can naturally evolve with the ongoing advancements in LLM data preparation, benefiting from improvements in LLM training pipelines without additional manual effort.