Approximating Human-Like Few-shot Learning with GPT-based Compression
Cynthia Huang Yuqing Xie Zhiying Jiang Jimmy Lin Ming Li
In this work, we conceptualize the learning process as information
compression. We seek to equip generative pre-trained models with human-like
learning capabilities that enable data compression during inference. We present
a novel approach that utilizes the Generative Pre-trained Transformer (GPT) to
approximate Kolmogorov complexity, with the aim of estimating the optimal
Information Distance for few-shot learning. We first propose using GPT as a
prior for lossless text compression, achieving a noteworthy compression ratio.
Experiment with LLAMA2-7B backbone achieves a compression ratio of 15.5 on
enwik9. We justify the pre-training objective of GPT models by demonstrating
its equivalence to the compression length, and, consequently, its ability to
approximate the information distance for texts. Leveraging the approximated
information distance, our method allows the direct application of GPT models in
quantitative text similarity measurements. Experiment results show that our
method overall achieves superior performance compared to embedding and prompt
baselines on challenging NLP tasks, including semantic similarity, zero and
one-shot text classification, and zero-shot text ranking.