Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains

ACL ARR 2025 July Submission836 Authors

Abstract: The rise of Large Language Models (LLMs) has redefined Machine Translation (MT), enabling context-aware and fluent translations across hundreds of languages and textual domains. Despite their remarkable capabilities, LLMs often exhibit uneven performance across language families and specialized domains. Moreover, recent evidence reveals that these models can encode and amplify different biases present in their training data, posing serious concerns for fairness, especially in low-resource languages. To address these gaps, we introduce Translation Tangles, a unified framework and dataset for evaluating the translation quality and fairness of open-source LLMs. Our approach benchmarks 24 bidirectional language pairs across multiple domains using different metrics. We further propose a hybrid bias detection pipeline that integrates rule-based heuristics, semantic similarity filtering, and LLM-based validation. We also introduce a high-quality, bias-annotated dataset based on human evaluations of 1,439 translation-reference pairs. The code and dataset are accessible on GitHub: https://anonymous.4open.science/r/TranslationTangles-EABE/

Paper Type: Long

Research Area: Machine Translation

Research Area Keywords: multilingual MT, few-shot/zero-shot MT, benchmarking, biases, human evaluation, NLP datasets

Contribution Types: Model analysis & interpretability, Data resources, Data analysis

Languages Studied: English, Czech, German, French, Russian, Finnish, Lithuanian, Estonian, Gujarati, Kazakh, Bangla, Chinese, Turkish

Previous URL: https://openreview.net/forum?id=A8t5YqOd9f

Reassignment Request Area Chair: No, I want the same area chair from our previous submission (subject to their availability).

Reassignment Request Reviewers: No, I want the same set of reviewers from our previous submission (subject to their availability)

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: N/A

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: Section 4, Appendix C

B2 Discuss The License For Artifacts: N/A

B3 Artifact Use Consistent With Intended Use: N/A

B4 Data Contains Personally Identifying Info Or Offensive Content: N/A

B5 Documentation Of Artifacts: N/A

B6 Statistics For Data: Yes

B6 Elaboration: Section 6

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: Section 4

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: Section 3, Section 4

C3 Descriptive Statistics: Yes

C3 Elaboration: Section 5

C4 Parameters For Packages: Yes

C4 Elaboration: Section 3, Section 4

D Human Subjects Including Annotators: Yes

D1 Instructions Given To Participants: Yes

D1 Elaboration: Section 6

D2 Recruitment And Payment: N/A

D3 Data Consent: N/A

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: N/A

E Ai Assistants In Research Or Writing: Yes

E1 Information About Use Of Ai Assistants: N/A

Author Submission Checklist: yes

Submission Number: 836