From Monolingual to Bilingual: Investigating Language Conditioning in Large Language Models for Psycholinguistic Tasks

• URL: http://arxiv.org/abs/2508.02502v1
• Date: Mon, 04 Aug 2025 15:10:44 GMT
• Title: From Monolingual to Bilingual: Investigating Language Conditioning in Large Language Models for Psycholinguistic Tasks
• Authors: Shuzhou Yuan, Zhan Qu, Mario Tawfelis, Michael Färber,
• Abstract summary: Large Language Models (LLMs) exhibit strong linguistic capabilities, but little is known about how they encode psycholinguistic knowledge across languages.<n>We investigate whether and how LLMs exhibit human-like psycholinguistic responses under different linguistic identities.<n>We evaluate two models, Llama-3.3-70B-Instruct and Qwen2.5-72B-Instruct, under monolingual and bilingual prompting in English, Dutch, and Chinese.
• Score: 9.837135712999471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) exhibit strong linguistic capabilities, but little is known about how they encode psycholinguistic knowledge across languages. We investigate whether and how LLMs exhibit human-like psycholinguistic responses under different linguistic identities using two tasks: sound symbolism and word valence. We evaluate two models, Llama-3.3-70B-Instruct and Qwen2.5-72B-Instruct, under monolingual and bilingual prompting in English, Dutch, and Chinese. Behaviorally, both models adjust their outputs based on prompted language identity, with Qwen showing greater sensitivity and sharper distinctions between Dutch and Chinese. Probing analysis reveals that psycholinguistic signals become more decodable in deeper layers, with Chinese prompts yielding stronger and more stable valence representations than Dutch. Our results demonstrate that language identity conditions both output behavior and internal representations in LLMs, providing new insights into their application as models of cross-linguistic cognition.

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