@inproceedings{iyer-etal-2026-beyond,
title = "Beyond Tokens: Concept-Level Training Objectives for {LLM}s",
author = "Iyer, Laya and
Somani, Pranav and
Guo, Alice and
Jurafsky, Dan and
Shani, Chen",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-short.34/",
pages = "457--474",
ISBN = "979-8-89176-381-4",
abstract = "The next-token prediction (NTP) objective has been foundational in the development of modern large language models (LLMs), driving advances in fluency and generalization. However, NTP operates at the token level, treating deviations from a single reference continuation as errors even when alternative continuations are equally plausible or semantically equivalent. As a result, token-level loss can penalize valid abstractions, paraphrases, or conceptually correct reasoning paths, biasing models toward surface form rather than underlying meaning. This mismatch between the training signal and semantic correctness motivates learning objectives that operate over higher-level representations.We propose a shift from token-level to concept-level prediction, where concepts group multiple surface forms of the same idea (e.g., ``mom,'' ``mommy,'' ``mother'' $\rightarrow$ \textit{MOTHER}). We introduce various methods for integrating conceptual supervision into LLM training and show that concept-aware models achieve lower perplexity, improved robustness under domain shift, and stronger performance than NTP-based models on diverse NLP benchmarks. This suggests concept-level supervision as an improved training signal that better aligns LLMs with human semantic abstractions."
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<abstract>The next-token prediction (NTP) objective has been foundational in the development of modern large language models (LLMs), driving advances in fluency and generalization. However, NTP operates at the token level, treating deviations from a single reference continuation as errors even when alternative continuations are equally plausible or semantically equivalent. As a result, token-level loss can penalize valid abstractions, paraphrases, or conceptually correct reasoning paths, biasing models toward surface form rather than underlying meaning. This mismatch between the training signal and semantic correctness motivates learning objectives that operate over higher-level representations.We propose a shift from token-level to concept-level prediction, where concepts group multiple surface forms of the same idea (e.g., “mom,” “mommy,” “mother” \rightarrow MOTHER). We introduce various methods for integrating conceptual supervision into LLM training and show that concept-aware models achieve lower perplexity, improved robustness under domain shift, and stronger performance than NTP-based models on diverse NLP benchmarks. This suggests concept-level supervision as an improved training signal that better aligns LLMs with human semantic abstractions.</abstract>
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%0 Conference Proceedings
%T Beyond Tokens: Concept-Level Training Objectives for LLMs
%A Iyer, Laya
%A Somani, Pranav
%A Guo, Alice
%A Jurafsky, Dan
%A Shani, Chen
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F iyer-etal-2026-beyond
%X The next-token prediction (NTP) objective has been foundational in the development of modern large language models (LLMs), driving advances in fluency and generalization. However, NTP operates at the token level, treating deviations from a single reference continuation as errors even when alternative continuations are equally plausible or semantically equivalent. As a result, token-level loss can penalize valid abstractions, paraphrases, or conceptually correct reasoning paths, biasing models toward surface form rather than underlying meaning. This mismatch between the training signal and semantic correctness motivates learning objectives that operate over higher-level representations.We propose a shift from token-level to concept-level prediction, where concepts group multiple surface forms of the same idea (e.g., “mom,” “mommy,” “mother” \rightarrow MOTHER). We introduce various methods for integrating conceptual supervision into LLM training and show that concept-aware models achieve lower perplexity, improved robustness under domain shift, and stronger performance than NTP-based models on diverse NLP benchmarks. This suggests concept-level supervision as an improved training signal that better aligns LLMs with human semantic abstractions.
%U https://aclanthology.org/2026.eacl-short.34/
%P 457-474
Markdown (Informal)
[Beyond Tokens: Concept-Level Training Objectives for LLMs](https://aclanthology.org/2026.eacl-short.34/) (Iyer et al., EACL 2026)
ACL
- Laya Iyer, Pranav Somani, Alice Guo, Dan Jurafsky, and Chen Shani. 2026. Beyond Tokens: Concept-Level Training Objectives for LLMs. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 457–474, Rabat, Morocco. Association for Computational Linguistics.