T o W : Thoughts of Words Improve Reasoning in Large Language Models (2025)

Abstract: We introduce thoughts of words (ToW), a novel training-time data-augmentation method for next-word prediction. ToW views next-word prediction as a core reasoning task and injects fine-grained thoughts explaining what the next word should be and how it is related to the previous contexts in pre-training texts. Our formulation addresses two fundamental drawbacks of existing next-word prediction learning schemes: they induce factual hallucination and are inefficient for models to learn the implicit reasoning processes in raw texts. While there are many ways to acquire such thoughts of words, we explore the first step of acquiring ToW annotations through distilling from larger models. After continual pre-training with only 70K ToW annotations, we effectively improve models’ reasoning performances by 7% to 9% on average and reduce model hallucination by up to 10%. At the same time, ToW is entirely agnostic to tasks and applications, introducing no additional biases on labels or semantics.

## introduction in this work, we explore a novel training-time data-augmentation method 1 called thoughts of words (tow), which injects fine-grained thoughts directly into the next-word prediction task and teaches the model to understand how the observed next word is related to previous contexts. unlike other data augmentation methods @xcite that annotate fine-grained explanations with respect to a task, tow directly views next-word prediction as a core reasoning task and hypothesizes that there is an abundance of natural information in next-words that we can harvest to improve models' reasoning capabilities. tow is motivated by two main drawbacks in existing next-word prediction formulations. first, because bruce lee met with the kung-fu movie director at noon regarding his martial arts education; the lunch lasted 2 hours. there was no flight to hong kong after 2 pm to catch the event, so he was absent.