Can Models Learn Skill Composition from Examples?
Authors: name: Haoyu Zhao, name: Simran Kaur, name: Dingli Yu, name: Anirudh Goyal, name: Sanjeev Arora

As large language models (LLMs) become increasingly advanced, their ability
to exhibit compositional generalization -- the capacity to combine learned
skills in novel ways not encountered during training -- has garnered
significant attention. This type of generalization, particularly in scenarios
beyond training data, is also of great interest in the study of AI safety and
alignment. A recent study introduced the SKILL-MIX evaluation, where models are
tasked with composing a short paragraph demonstrating the use of a specified
k-tuple of language skills. While small models struggled with composing even
with k=3, larger models like GPT-4 performed reasonably well with k=5 and
6.
  In this paper, we employ a setup akin to SKILL-MIX to evaluate the capacity
of smaller models to learn compositional generalization from examples.
Utilizing a diverse set of language skills -- including rhetorical, literary,
reasoning, theory of mind, and common sense -- GPT-4 was used to generate text
samples that exhibit random subsets of k skills. Subsequent fine-tuning of 7B
and 13B parameter models on these combined skill texts, for increasing values
of k, revealed the following findings: (1) Training on combinations of k=2
and 3 skills results in noticeable improvements in the ability to compose
texts with k=4 and 5 skills, despite models never having seen such examples
during training. (2) When skill categories are split into training and held-out
groups, models significantly improve at composing texts with held-out skills
during testing despite having only seen training skills during fine-tuning,
illustrating the efficacy of the training approach even with previously unseen
skills. This study also suggests that incorporating skill-rich (potentially
synthetic) text into training can substantially enhance the compositional
capabilities of models.