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

arXiv: https://arxiv.org/abs/2508.02502 · HTML: https://mlsanity.com/paper/2508.02502/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Information-Theoretic Probing for Linguistic Structure](https://mlsanity.com/paper/2004.03061/) (2020)
- [Probing Pretrained Language Models for Lexical Semantics](https://mlsanity.com/paper/2010.05731/) (2020)
- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Probing LLMs for hate speech detection: strengths and vulnerabilities](https://mlsanity.com/paper/2310.12860/) (2023)
- [Probing the Emergence of Cross-lingual Alignment during LLM Training](https://mlsanity.com/paper/2406.13229/) (2024)
- [Discourse Probing of Pretrained Language Models](https://mlsanity.com/paper/2104.05882/) (2021)
- [The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative](https://mlsanity.com/paper/2210.13181/) (2022)
- [A Psycholinguistic Evaluation of Language Models' Sensitivity to Argument Roles](https://mlsanity.com/paper/2410.16139/) (2024)
- [On the Proper Treatment of Tokenization in Psycholinguistics](https://mlsanity.com/paper/2410.02691/) (2024)
- [Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability](https://mlsanity.com/paper/2409.15827/) (2024)
- [Large Language Models for Psycholinguistic Plausibility Pretesting](https://mlsanity.com/paper/2402.05455/) (2024)
- [Psycholinguistic Word Features: a New Approach for the Evaluation of LLMs Alignment with Humans](https://mlsanity.com/paper/2506.22439/) (2025)
- [Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning](https://mlsanity.com/paper/2303.16445/) (2023)

## Built on by (most cited first, 1 of 1)

- [Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://mlsanity.com/paper/2601.14004/) (2026)

## Abstract (arXiv)

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.