arXiv:2511.03559v1 Announce Type: new 
Abstract: This paper presents the first empirical demonstration of controllable locality in transformer language models, a novel architectural framework that enables continuous control over the degree of representation localization through a tunable locality dial parameter. Unlike traditional language models that rely exclusively on distributed representations, our approach allows dynamic interpolation between highly interpretable localist encodings and efficient distributed representations without requiring model retraining. We conducted experiments on the WikiText corpus using a two-layer transformer architecture, systematically varying the locality parameter {\lambda} across the full spectrum from 1.0 (fully localist) to 0.0 (fully distributed). Our results demonstrate that localist configurations achieve dramatically lower attention entropy, with {\lambda} = 1.0 yielding 5.36 bits compared to 7.18 bits at {\lambda} = 0.0, while maintaining substantially higher pointer fidelity scores reflecting stronger alignment with rule-specified targets. Prediction experiments reveal that intermediate locality values optimize the tradeoff between interpretability and performance, with {\lambda} = 0.6 achieving test perplexity of 4.65 and accuracy of 84.7%. These findings establish that localist language models provide a practical framework for applications in regulated domains requiring both transparency and capability, offering precise mathematical control over the interpretability-performance spectrum through explicit penalty thresholds and information-theoretic design principles.

قدمت ورقة بحثية حديثة تجارب رائدة مع نماذج اللغة المحلية، مما يظهر طريقة جديدة للتحكم في كيفية تمثيل اللغة. تتيح هذه الطريقة المبتكرة للباحثين ضبط درجة تمثيل المحلية، مما يسهل تفسير وفهم معالجة اللغة. هذا التطور مهم لأنه يمكن أن يعزز أداء ونطاق تطبيق نماذج اللغة في مجالات مختلفة، مما يمهد الطريق لأدوات تواصل وتطبيقات ذكاء اصطناعي أكثر فعالية.

Un artículo reciente ha presentado experimentos innovadores con modelos de lenguaje localistas, mostrando una nueva forma de controlar cómo se representa el lenguaje. Este enfoque innovador permite a los investigadores ajustar el grado de localización de la representación, facilitando la interpretación y comprensión del procesamiento del lenguaje. Este desarrollo es significativo, ya que podría mejorar el rendimiento y la aplicabilidad de los modelos de lenguaje en varios campos, allanando el camino para herramientas de comunicación y aplicaciones de IA más efectivas.

Un article récent a présenté des expériences révolutionnaires avec des modèles de langage localistes, montrant une nouvelle façon de contrôler la représentation du langage. Cette approche innovante permet aux chercheurs d'ajuster le degré de localisation de la représentation, facilitant ainsi l'interprétation et la compréhension du traitement du langage. Ce développement est important car il pourrait améliorer les performances et l'applicabilité des modèles de langage dans divers domaines, ouvrant la voie à des outils de communication et des applications d'IA plus efficaces.

A recent paper has introduced groundbreaking experiments with localist language models, showcasing a new way to control how language is represented. This innovative approach allows researchers to adjust the degree of representation localization, making it easier to interpret and understand language processing. This development is significant as it could enhance the performance and applicability of language models in various fields, paving the way for more effective communication tools and AI applications.

AILA--First Experiments with Localist Language Models

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AILA--First Experiments with Localist Language Models

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