# Multilingual Relative Clause Attachment Ambiguity Resolution in Large Language Models

arXiv: https://arxiv.org/abs/2503.02971 · HTML: https://mlsanity.com/paper/2503.02971/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://mlsanity.com/paper/2402.10588/) (2024)
- [Hallucinations in Large Multilingual Translation Models](https://mlsanity.com/paper/2303.16104/) (2023)
- [Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries](https://mlsanity.com/paper/2310.13132/) (2023)
- [On the Sensitivity and Stability of Model Interpretations in NLP](https://mlsanity.com/paper/2104.08782/) (2021)
- [Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models](https://mlsanity.com/paper/1909.10579/) (2019)
- [Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment](https://mlsanity.com/paper/2005.00165/) (2020)
- [A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English](https://mlsanity.com/paper/2011.00960/) (2020)
- [SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser](https://mlsanity.com/paper/2403.07202/) (2024)
- [Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge](https://mlsanity.com/paper/2102.00924/) (2021)

## Abstract (arXiv)

This study examines how large language models (LLMs) resolve relative clause (RC) attachment ambiguities and compares their performance to human sentence processing. Focusing on two linguistic factors, namely the length of RCs and the syntactic position of complex determiner phrases (DPs), we assess whether LLMs can achieve human-like interpretations amid the complexities of language. In this study, we evaluated several LLMs, including Claude, Gemini and Llama, in multiple languages: English, Spanish, French, German, Japanese, and Korean. While these models performed well in Indo-European languages (English, Spanish, French, and German), they encountered difficulties in Asian languages (Japanese and Korean), often defaulting to incorrect English translations. The findings underscore the variability in LLMs' handling of linguistic ambiguities and highlight the need for model improvements, particularly for non-European languages. This research informs future enhancements in LLM design to improve accuracy and human-like processing in diverse linguistic environments.