Limits of Transformer Language Models on Learning to Compose Algorithms
<|reference_start|>Limits of Transformer Language Models on Learning to Compose Algorithms: 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. On both training LLaMA models from scratch and prompting on GPT-4 and Gemini, 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.<|reference_end|>
@article{thomm2024limits,
    title={Limits of Transformer Language Models on Learning to Compose Algorithms},
    author={Jonathan Thomm, Giacomo Camposampiero, Aleksandar Terzic, Michael
  Hersche, Bernhard Sch"olkopf, Abbas Rahimi},
    journal={arXiv preprint arXiv:2402.05785},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05785},
    primaryClass={cs.LG cs.AI cs.CL}
}