@inproceedings{xu-etal-2025-tow,
title = "{T}o{W}: Thoughts of Words Improve Reasoning in Large Language Models",
author = "Xu, Zhikun and
Shen, Ming and
Dineen, Jacob and
Li, Zhaonan and
Ye, Xiao and
Lu, Shijie and
Rrv, Aswin and
Baral, Chitta and
Zhou, Ben",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.157/",
doi = "10.18653/v1/2025.naacl-long.157",
pages = "3057--3075",
ISBN = "979-8-89176-189-6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T ToW: Thoughts of Words Improve Reasoning in Large Language Models
%A Xu, Zhikun
%A Shen, Ming
%A Dineen, Jacob
%A Li, Zhaonan
%A Ye, Xiao
%A Lu, Shijie
%A Rrv, Aswin
%A Baral, Chitta
%A Zhou, Ben
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F xu-etal-2025-tow
%X 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.
%R 10.18653/v1/2025.naacl-long.157
%U https://aclanthology.org/2025.naacl-long.157/
%U https://doi.org/10.18653/v1/2025.naacl-long.157
%P 3057-3075
Markdown (Informal)
[ToW: Thoughts of Words Improve Reasoning in Large Language Models](https://aclanthology.org/2025.naacl-long.157/) (Xu et al., NAACL 2025)
ACL
- Zhikun Xu, Ming Shen, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin Rrv, Chitta Baral, and Ben Zhou. 2025. ToW: Thoughts of Words Improve Reasoning in Large Language Models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3057–3075, Albuquerque, New Mexico. Association for Computational Linguistics.