Transformer language models have received widespread public attention, yet their
generated text is often surprising even to NLP researchers. In this survey, we discuss over …

Given the intractably large size of the space of proofs, any model that is capable of general
deductive reasoning must generalize to proofs of greater complexity. Recent studies have …

Compositional generalization--understanding unseen combinations of seen primitives--is an
essential reasoning capability in human intelligence. The AI community mainly studies this …

Large-scale models trained on extensive datasets, have emerged as the preferred approach
due to their high generalizability across various tasks. In-context learning (ICL), a popular …

Transformers trained on huge text corpora exhibit a remarkable set of capabilities, eg,
performing simple logical operations. Given the inherent compositional nature of language …

In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot
nature and improved generalization. However, learning to parse to rare domain-specific …

NLP models have progressed drastically in recent years, according to numerous datasets
proposed to evaluate performance. Questions remain, however, about how particular …

Cross-domain and cross-compositional generalization of Text-to-SQL semantic parsing is a
challenging task. Existing Large Language Model (LLM) based solutions rely on inference …

We are amidst an explosion of artificial intelligence research, particularly around large
language models (LLMs). These models have a range of applications across domains like …

Transformers can under some circumstances generalize to novel problem instances whose
constituent parts might have been encountered during training but whose compositions …