@inproceedings{lepori-etal-2026-algorithmic,
title = "An Algorithmic Account of Few-Shot Concept Induction in Language Models",
author = "Lepori, Michael A. and
Johnston, Laila and
Sathe, Aalok and
Yang, Zhuonan and
Feiman, Roman and
Pavlick, Ellie",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2102/",
doi = "10.18653/2026.findings-emnlp.2102",
pages = "44753--44773",
ISBN = "979-8-89176-462-0",
abstract = "Both models and minds not only encode features that describe an entity, but can specifically represent which features of that entity are relevant for a given context. This core capability underlies in-context learning in language models and a variety of cognitive phenomena in minds, yet it is unclear how intelligent systems identify and promote relevant features. In this work, we propose Amortized Direction Accentuation (ADA), an algorithmic hypothesis that provides an explanation for how relevance is determined in the context of few-shot concept induction{---}the ability to infer a latent concept from a small number of positive examples. Under ADA, concept induction proceeds by forming a summary representation of the examples in a feature space that is shaped by amortized knowledge about which features tend to be useful for finding commonalities between items. We investigate and validate ADA in language models using a few-shot concept induction task. This work provides a novel perspective on the mechanisms supporting contextualization in language models and a promising hypothesis for a core aspect of cognition."
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%0 Conference Proceedings
%T An Algorithmic Account of Few-Shot Concept Induction in Language Models
%A Lepori, Michael A.
%A Johnston, Laila
%A Sathe, Aalok
%A Yang, Zhuonan
%A Feiman, Roman
%A Pavlick, Ellie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lepori-etal-2026-algorithmic
%X Both models and minds not only encode features that describe an entity, but can specifically represent which features of that entity are relevant for a given context. This core capability underlies in-context learning in language models and a variety of cognitive phenomena in minds, yet it is unclear how intelligent systems identify and promote relevant features. In this work, we propose Amortized Direction Accentuation (ADA), an algorithmic hypothesis that provides an explanation for how relevance is determined in the context of few-shot concept induction—the ability to infer a latent concept from a small number of positive examples. Under ADA, concept induction proceeds by forming a summary representation of the examples in a feature space that is shaped by amortized knowledge about which features tend to be useful for finding commonalities between items. We investigate and validate ADA in language models using a few-shot concept induction task. This work provides a novel perspective on the mechanisms supporting contextualization in language models and a promising hypothesis for a core aspect of cognition.
%R 10.18653/2026.findings-emnlp.2102
%U https://aclanthology.org/2026.findings-emnlp.2102/
%U https://doi.org/10.18653/2026.findings-emnlp.2102
%P 44753-44773
Markdown (Informal)
[An Algorithmic Account of Few-Shot Concept Induction in Language Models](https://aclanthology.org/2026.findings-emnlp.2102/) (Lepori et al., Findings 2026)
ACL