@inproceedings{
thomm2024limits,
title={Limits of Transformer Language Models on Learning to Compose Algorithms},
author={Jonathan Thomm and Giacomo Camposampiero and Aleksandar Terzic and Michael Hersche and Bernhard Sch{\"o}lkopf and Abbas Rahimi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=x7AD0343Jz}
}
We analyze the capabilities of Transformer language models in learning compositional discrete tasks. To this end, we evaluate training LLaMA models and prompting GPT-4 and Gemini on four tasks demanding to learn a composition of several discrete sub-tasks. In particular, we measure how well these models can reuse primitives observable in the sub-tasks to learn the composition task. Our results indicate that compositional learning in state-of-the-art Transformer language models is highly sample inefficient: LLaMA requires more data samples than relearning all sub-tasks from scratch to learn the compositional task; in-context prompting with few samples is unreliable and fails at executing the sub-tasks or correcting the errors in multi-round code generation. Further, by leveraging complexity theory, we support these findings with a theoretical analysis focused on the sample inefficiency of gradient descent in memorizing feedforward models. We open source our code at https://github.com/IBM/limitations-lm-algorithmic-compositional-learning.
Limits of Transformer Language Models on Learning to Compose Algorithms
Jonathan Thomm Giacomo Camposampiero Aleksandar Terzic Michael Hersche Bernhard Schölkopf Abbas Rahimi