| Challenge: | Recent studies indicate that multilingual language models utilize structural similarities between languages to facilitate cross-lingual transfer. |
| Approach: | They propose a multilingual model that uses structural similarities between languages to facilitate cross-lingual transfer by a meaningful bias towards sentence length and cross-linguistic differences. |
| Outcome: | The proposed model can predict varied patterns for 13 languages, despite being fine-tuned only on English data. |
Similar Papers
Scaling in Cognitive Modelling: a Multilingual Approach to Human Reading Times (2023.acl-short)
Copied to clipboard
| Challenge: | Neural language models provide conditional probability distributions over the lexicon that are predictive of human processing times. |
| Approach: | They propose to use a transformer-based model to generate probabilistic estimates that are less predictive of early eye-tracking measurements reflecting lexical access and early semantic integration. |
| Outcome: | The proposed models show that larger models capture late eye-tracking measurements that reflect the full integration of a word into the current language context. |
Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer (2026.acl-srw)
Copied to clipboard
| Challenge: | Large language models (LLMs) have advanced natural language processing, yet their benefits remain concentrated in English and a small number of high-resource languages. |
| Approach: | They fine-tuned large language models (4B–671B parameters) on Arabic and evaluated zero-shot reading comprehension on Semitic languages and non-Semitic controls. |
| Outcome: | The results show that models with weak baselines improve across all languages, whereas strong-baseline models show only marginal gains regardless of language family. |
7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, but studies on risks associated with cross biases are limited to immediate context preferences. |
| Approach: | They investigate multilingual bias in state-of-the-art Large Language Models by analyzing their responses to decision-making tasks across multiple languages. |
| Outcome: | The proposed model can provide personalized advice across university applications, travel, and relocation scenarios. |
On the Acquisition of Shared Grammatical Representations in Bilingual Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Crosslingual transfer is crucial to contemporary language models’ multilingual capabilities, but how it occurs is not well understood. |
| Approach: | They use structural priming to study grammatical representations in humans by controlling for training data quantity and language exposure. |
| Outcome: | The proposed model is able to learn a language in two languages and has a higher likelihood of learning a prepositional object (PO) dative sentence than a double object (DO) . |
Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter (2025.findings-acl)
Copied to clipboard
| Challenge: | Prior work on cross-lingual transfer often focuses on a small set of languages from a few language families and/or a single task. |
| Approach: | They analyze cross-lingual transfer for 263 languages from a wide variety of language families . they include three popular NLP tasks: POS tagging, dependency parsing, topic classification . |
| Outcome: | The proposed approach is based on linguistic similarity measures for 263 languages . the results show that the effect of linguistic similarities on transfer performance depends on a range of factors . |
Multilingual Language Models Predict Human Reading Behavior (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that cognitively motivated "attention" mechanism in neural models is not a good indicator for relative importance. |
| Approach: | They compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing. |
| Outcome: | The proposed models predict reading time measures on Dutch, English, German, and Russian texts. |
Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Abstract grammatical knowledge is key to linguistic generalization in humans . strong evidence for grammatikal abstraction in humans comes from structural priming . |
| Approach: | They compare human models of crosslingual structural priming to human models . they find evidence for abstract monolingual and crosslingual grammatical representations . |
| Outcome: | The results show that grammatical representations in multilingual models are similar to humans . the strongest evidence for grammatikal abstraction in humans comes from structural priming . |
Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on multilingual models have focused on their cross-lingual transfer behavior . a recent study examined multilingual model learning from the multilingual pretraining signal . |
| Approach: | They analyze checkpoints during multilingual pretraining to identify when models acquire in-language and cross-lingual abilities. |
| Outcome: | The proposed model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones. |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space (2021.emnlp-main)
Copied to clipboard
| Challenge: | Cross-lingual language models house representations for many different languages in the same space. |
| Approach: | They investigate linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. |
| Outcome: | The results show that word order agreement and agreement in morphological complexity are strongest predictors of cross-linguality. |