{"ID":2872874,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.07389","arxiv_id":"2509.07389","title":"Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents","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.","short_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 pat...","url_abs":"https://arxiv.org/abs/2509.07389","url_pdf":"https://arxiv.org/pdf/2509.07389v1","authors":"[\"Sankalp Tattwadarshi Swain\",\"Anshika Krishnatray\",\"Dhruv Kumar\",\"Jagat Sesh Challa\"]","published":"2025-09-09T05:09:27Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.HC\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}