Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents

• URL: http://arxiv.org/abs/2509.07389v1
• Date: Tue, 09 Sep 2025 05:09:27 GMT
• Title: Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
• Authors: Sankalp Tattwadarshi Swain, Anshika Krishnatray, Dhruv Kumar, Jagat Sesh Challa,
• Abstract summary: We assess whether large language models can acquire a language through pattern recognition and interactive feedback.<n>Our findings show that LLM agents fail to establish a conversation within 100 responses.<n>Results suggest a new direction for evaluation benchmarks and open pathways to model designs that learn more effectively from interactive feedback.
• Score: 1.2802720336459552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing evaluation studies on linguistic competence of large language models (LLM agents) have focused primarily on vocabulary learning, morphological rule induction, syntactic generalization, pragmatic inference, and cross-linguistic transfer. However, none assess whether LLM agents can acquire a language through pattern recognition and interactive feedback, a central feature of human language acquisition. We propose a novel experimental framework in which an LLM agent is evaluated on its ability to acquire and use a newly constructed language (Tinkatongue) in conversation with a bot that understands only Tinkatongue. Our findings show that LLM agents fail to establish a conversation within 100 responses, yet they adopt distinct strategies that mirror human approaches to language learning. The results suggest a new direction for evaluation benchmarks and open pathways to model designs that learn more effectively from interactive feedback.

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