@inproceedings{yang-etal-2026-training,
title = "Training {LLM}s Beyond Next Token Prediction - Filling the Mutual Information Gap",
author = "Yang, Chun-Hao and
Feng, Bo-Han and
Lai, Tzu-Yuan and
Chen, Yan-Yu and
Huang, Yin-Kai Dean and
Lin, Shou-De",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.248/",
pages = "5539--5554",
ISBN = "979-8-89176-439-2",
abstract = "Autoregressive language models are usually trained with next-token prediction under a fixed left-to-right target order. This paper studies target-token organization as a training-time design choice across arithmetic reasoning, multi-label text classification, and text generation. Controlled order studies show that the default NTP order can be far from the best-performing tested organization, while fixed hand-crafted orders such as reverse order do not generalize across tasks. To address this, Max(MI(S; t)) is introduced as a deterministic mutual-information-based heuristic that prioritizes information-rich target tokens. For fixed-length outputs, it constructs task-specific prediction orders; for open-vocabulary text generation, the same principle is applied through lightweight prefix augmentation. Experiments with GPT-style models and pretrained Llama/Qwen variants show that Max(MI(S; t)) often improves over standard NTP and simple baselines such as reverse order, TF-IDF, and conditional-probability selection, without changing model architecture or autoregressive decoding."
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<abstract>Autoregressive language models are usually trained with next-token prediction under a fixed left-to-right target order. This paper studies target-token organization as a training-time design choice across arithmetic reasoning, multi-label text classification, and text generation. Controlled order studies show that the default NTP order can be far from the best-performing tested organization, while fixed hand-crafted orders such as reverse order do not generalize across tasks. To address this, Max(MI(S; t)) is introduced as a deterministic mutual-information-based heuristic that prioritizes information-rich target tokens. For fixed-length outputs, it constructs task-specific prediction orders; for open-vocabulary text generation, the same principle is applied through lightweight prefix augmentation. Experiments with GPT-style models and pretrained Llama/Qwen variants show that Max(MI(S; t)) often improves over standard NTP and simple baselines such as reverse order, TF-IDF, and conditional-probability selection, without changing model architecture or autoregressive decoding.</abstract>
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%0 Conference Proceedings
%T Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap
%A Yang, Chun-Hao
%A Feng, Bo-Han
%A Lai, Tzu-Yuan
%A Chen, Yan-Yu
%A Huang, Yin-Kai Dean
%A Lin, Shou-De
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F yang-etal-2026-training
%X Autoregressive language models are usually trained with next-token prediction under a fixed left-to-right target order. This paper studies target-token organization as a training-time design choice across arithmetic reasoning, multi-label text classification, and text generation. Controlled order studies show that the default NTP order can be far from the best-performing tested organization, while fixed hand-crafted orders such as reverse order do not generalize across tasks. To address this, Max(MI(S; t)) is introduced as a deterministic mutual-information-based heuristic that prioritizes information-rich target tokens. For fixed-length outputs, it constructs task-specific prediction orders; for open-vocabulary text generation, the same principle is applied through lightweight prefix augmentation. Experiments with GPT-style models and pretrained Llama/Qwen variants show that Max(MI(S; t)) often improves over standard NTP and simple baselines such as reverse order, TF-IDF, and conditional-probability selection, without changing model architecture or autoregressive decoding.
%U https://aclanthology.org/2026.aacl-long.248/
%P 5539-5554
Markdown (Informal)
[Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap](https://aclanthology.org/2026.aacl-long.248/) (Yang et al., AACL-IJCNLP 2026)
ACL
- Chun-Hao Yang, Bo-Han Feng, Tzu-Yuan Lai, Yan-Yu Chen, Yin-Kai Dean Huang, and Shou-De Lin. 2026. Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5539–5554, Hengqin, China. Association for Computational Linguistics.