To See the Unseen: on the Generalization Ability of Transformers in Symbolic Reasoning
We investigate the ability of decoder-only transformer models to perform abstract symbolic reasoning; specifically solving propositional logic reasoning problems given in-context. Previous work demonstrated that models fail to generalize to problems involving variable names that were not observed during training, and it was shown that one reason behind this is the difficulty of copying (or generating) unseen tokens. We show both theoretically and empirically that a particular representational collapse also has a crucial role: the unembeddings (last-layer weights) of unseen tokens collapse to nearly the same vector during training. The collapse makes distinguishing multiple unseen variable...
| Authors | Nevena Lazić, Liam Fowl, András György et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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