---
title: "Concept Tokens — paper by Sastre and Rosá (2026)"
description: "The authors propose Concept Tokens. 23 references, cited by 1 paper here. Among them: 0."
authors: ["Ignacio Sastre", "Aiala Rosá"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1319/"
doi: "10.18653/v1/2026.findings-acl.1319"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sastre-2026-concept-tokens-learning-behavioral-embeddings/"
---
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# Concept Tokens: Learning Behavioral Embeddings Through Concept Definitions

**Authors:** [Ignacio Sastre](https://md.research.elamur.ai/free/en/authors/ignacio-sastre/) and [Aiala Rosá](https://md.research.elamur.ai/free/en/authors/aiala-rosa/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1319/) · [PDF](https://aclanthology.org/2026.findings-acl.1319.pdf) · [DOI 10.18653/v1/2026.findings-acl.1319](https://doi.org/10.18653/v1/2026.findings-acl.1319) · [OpenAlex](https://openalex.org/W7166837361)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/sastre-2026-concept-tokens-learning-behavioral-embeddings/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether a frozen LLM can absorb a new concept from definitions alone, without labeled behavioral examples. The authors propose Concept Tokens. A single new special token is added to the vocabulary, and every mention of the target concept in a set of natural-language definitions is replaced by it. Only the token's input embedding is trained, using the ordinary language-modeling loss. Because several definitions must be predicted at once, the embedding is pushed toward a generalized representation rather than memorizing one text. Earlier memory tokens are a special case with a single definition.

Experiments use Llama 3.1 8B Instruct (4-bit) on three tasks:
- closed-book QA on HotpotQA,
- inducing recasting, a second-language feedback strategy,
- a qualitative case study.

Placing the token in a prompt works directionally: negating it suppresses the associated behavior, while asserting it amplifies it. In the recasting task, the token follows other instructions better than putting the whole definitional corpus in context. The motivation is compact, privacy-friendly steering for small on-premise models, such as those used in schools.

## Key points
- On HotpotQA, negating the hallucination token reduces hallucinated answers mostly by making the model abstain more often. Asserting the token increases hallucinations and lowers precision, so the gain is in coverage trade-off rather than answer reliability.
- In the recasting task, some prompting baselines enforce the behavior more strongly but break other instructions, such as asking follow-up questions or not correcting answers that are already right. The concept token preserves those instructions better. The dataset comes from Ceibal en Inglés.
- The qualitative study compares a token for the real Eiffel Tower with one for a fictional "Austral Tower" in Montevideo. The fictional token captures a coherent theme but invents details such as names, dates and heights, which suggests the method steers meaning rather than storing new facts.
- The definitional corpora were synthetically generated by stronger LLMs. Training is costly because it requires backpropagation through the whole network, and the work ran on limited compute (ClusterUY, Colab Pro). Code and data are at github.com/nsuruguay05/concept_tokens.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Universidad de la República de Uruguay](https://md.research.elamur.ai/free/en/universities/universidad-de-la-republica-de-uruguay/) — University, Montevideo, Uruguay · 9 works · 450 citations

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## Cite

Cite as: Ignacio Sastre and Aiala Rosá. 2026. Concept Tokens: Learning Behavioral Embeddings Through Concept Definitions. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sastre2026concept,
  title = {{Concept Tokens: Learning Behavioral Embeddings Through Concept Definitions}},
  author = {Ignacio Sastre and Aiala Rosá},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1319},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1319}
}
```

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## References

23 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 31 October 2026*