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    <abstract>A language model that can generate utterances that are appraised as being within a specific age of a young child who is beginning their language learning journey can be useful in scenarios where child-level language is needed, for example in virtual avatars, interactions with individuals who have disabilities, or developmental robotics. In this paper, we focus on an age range that is not represented in prior work: emergent speakers. We use the CHILDES database to train and tune language models of different parameter sizes using a group relative policy optimization reinforcement learning regime. Our goal is to find the most coherent, yet child-like language model while keeping the number of parameters to as few as possible. We evaluate using metrics of coherency, “toddlerality,” and an evaluation using human subjects who interact with two robot platforms. Our experiments show that even small language models (under 1 billion parameters) can be used effectively to generate child-like utterances.</abstract>
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%0 Conference Proceedings
%T Learning to Speak Like a Child: Reinforcing and Evaluating a Child-level Generative Language Model
%A Levandovsky, Enoch
%A Manaseryan, Anna
%A Kennington, Casey
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F levandovsky-etal-2025-learning
%X A language model that can generate utterances that are appraised as being within a specific age of a young child who is beginning their language learning journey can be useful in scenarios where child-level language is needed, for example in virtual avatars, interactions with individuals who have disabilities, or developmental robotics. In this paper, we focus on an age range that is not represented in prior work: emergent speakers. We use the CHILDES database to train and tune language models of different parameter sizes using a group relative policy optimization reinforcement learning regime. Our goal is to find the most coherent, yet child-like language model while keeping the number of parameters to as few as possible. We evaluate using metrics of coherency, “toddlerality,” and an evaluation using human subjects who interact with two robot platforms. Our experiments show that even small language models (under 1 billion parameters) can be used effectively to generate child-like utterances.
%U https://aclanthology.org/2025.sigdial-1.30/
%P 370-382

Markdown (Informal)

[Learning to Speak Like a Child: Reinforcing and Evaluating a Child-level Generative Language Model](https://aclanthology.org/2025.sigdial-1.30/) (Levandovsky et al., SIGDIAL 2025)

• Learning to Speak Like a Child: Reinforcing and Evaluating a Child-level Generative Language Model (Levandovsky et al., SIGDIAL 2025)

ACL

• Enoch Levandovsky, Anna Manaseryan, and Casey Kennington. 2025. Learning to Speak Like a Child: Reinforcing and Evaluating a Child-level Generative Language Model. In Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 370–382, Avignon, France. Association for Computational Linguistics.