Exploring Geometric Representational Disparities between Multilingual and Bilingual Translation Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing work shows that limited modeling capacity is a major contributor to reduced performance in multilingual models. |
| Approach: | They investigate the isotropy of multilingual model decoder representations using intrinsic dimensionality and IsoScore to measure how they utilize the dimensions in their underlying vector space. |
| Outcome: | The proposed model decoder representations are less isotropic and occupy fewer dimensions than bilingual models. |
Similar Papers
Correlations between Multilingual Language Model Geometry and Crosslingual Transfer Performance (2024.lrec-main)
Copied to clipboard
| Challenge: | Pre-trained multilingual language models represent multiple languages in a single vector space, a feature hypothesized to enable impressive crosslingual transfer capabilities. |
| Approach: | They propose to use a multilingual representation space that sorts axes based on their language-separability to determine whether geometric distances between languages correlate with crosslingual transfer performance. |
| Outcome: | The proposed measures do not generalize well across models, layers, and tasks. |
The Geometry of Multilingual Language Model Representations (2022.emnlp-main)
Copied to clipboard
| Challenge: | XLM-R models encode language-sensitive information in each language, allowing them to extract features for downstream tasks and cross-lingual transfer learning. |
| Approach: | They evaluate how multilingual language models maintain a shared multilingual representation space while still encoding language-sensitive information in each language. |
| Outcome: | The proposed model can extract features for downstream tasks and cross-lingual transfer learning. |
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) . |
Breaking Down Multilingual Machine Translation (2022.findings-acl)
Copied to clipboard
| Challenge: | Multilingual training is an essential ingredient in machine translation systems . but it has different effects in different multilingual settings, such as many-to-one, one-tomany and many- to-many learning . |
| Approach: | They compare multilingual training settings with encoders and decoders initialized by multilingual learning . they find important attention heads for each language pair and compare their correlations during inference . |
| Outcome: | The proposed models outperform the best models for high-resource languages and one-to-many models for low-resourced languages. |
Representational Isomorphism and Alignment of Multilingual Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing isomorphism of sentence representations can facilitate representational alignments in zero-shot and few-shot settings. |
| Approach: | They propose to apply a contrastive objective to LLMs with a small number of translation pairs to improve models' performance on Semantic Textual Similarity tasks. |
| Outcome: | The proposed representation-level approach significantly improves on Semantic Textual Similarity (STS) tasks across languages even without a monolingual objective. |
Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that pixel representations can be finetuned across scripts without vocabulary extensions, adapters, or transliteration. |
| Approach: | They propose to use pixel representations to train multilingual machine translation models . they explore parameter sharing within and across scripts to better understand where they lead to positive transfer . |
| Outcome: | The proposed model improves on two multilingual datasets with different language coverage compared to subword embeddings . the proposed model can be finetuned cross-lingually or to unseen scripts, and is more data-efficient than other alternatives such as vocabulary expansion . |
Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing evidence on the intrinsic difficulty of multilingual modeling is limited to small monolingual models or bilingual models trained from scratch. |
| Approach: | They propose to use typological properties to determine the difficulty of modeling a language . they analyze two large pre-trained multilingual translation models . |
| Outcome: | The proposed models are based on two large pre-trained models of encoder-decoder and decoder-only machine translation. |
On the Cross-lingual Transferability of Monolingual Representations (2020.acl-main)
Copied to clipboard
| Challenge: | State-of-the-art unsupervised multilingual models generalize in zero-shot cross-lingual setting . generalization ability attributed to shared subword vocabulary and joint training across multiple languages . |
| Approach: | They propose an approach that transfers a monolingual model to new languages at the lexical level. |
| Outcome: | The proposed approach is competitive with multilingual BERT on cross-lingual classification benchmarks and on a new cross-linguistic question answering dataset. |
Three Strategies to Improve One-to-Many Multilingual Translation (D18-1)
Copied to clipboard
| Challenge: | Existing studies show that one-to-many multilingual translation cannot perform on par with the individually trained models. |
| Approach: | They propose to exploit unique initial states for target languages and language-dependent positional embeddings to create hidden cells of the encoder to achieve comparable or even better performance than individually trained models. |
| Outcome: | The proposed methods achieve comparable or even better performance than the individually trained models. |
Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains (2026.findings-acl)
Copied to clipboard
Md. Faiyaz Abdullah Sayeedi, Subhey Sadi Rahman, Md. Mahbub Alam, Md. Adnanul Islam, Jannatul Ferdous Deepti, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
| Challenge: | Large Language Models (LLMs) have redefined Machine Translation, enabling context-aware and fluent translations across hundreds of languages and textual domains. |
| Approach: | They propose a framework and dataset to evaluate the translation quality and fairness of open-source LLMs. |
| Outcome: | The proposed framework and dataset evaluates translation quality and fairness of open-source LLMs. |