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New AlphaMWE Corpus Exposes LLM Translation Blind Spots for Multiword Expressions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed the AlphaMWE corpus to test the capabilities of large language models (LLMs) in machine translation, specifically focusing on Multiword Expressions (MWEs). The study evaluated 31 MT systems across various language pairs, including English to Chinese, Polish, German, and several Arabic dialects. Automatic evaluations using metrics like BLEU and BERT-score, followed by human evaluations, revealed that figurative language and MWEs continue to pose challenges for LLMs, and aggregate scores can mask language-specific errors. AI

IMPACT Highlights ongoing challenges for LLMs in nuanced language translation, suggesting areas for future model development.

RANK_REASON The item is a research paper detailing a new corpus and evaluation of LLM performance on machine translation tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AlphaMWE Corpus Exposes LLM Translation Blind Spots for Multiword Expressions

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The item is a research paper detailing a new corpus and evaluation of LLM performance on machine translation tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lifeng Han, Jiahui Liang, Anna Latusek, Karim El Haff, Amal Haddad Haddad, Josua H\"ofgen, Kilian Evang, Min Ma, Maryia Zhyrko ·

    Mind the Gap: Exposing LLM Translation Blind Spots Using the AlphaMWE Multilingual Parallel Corpus

    arXiv:2609.06634v1 Announce Type: cross Abstract: LLMs' performance on machine translation (MT) tasks is often dependent on the data availability in the specific domains and language pairs that they are trained upon. To examine if Multiword Expressions (MWEs) still set a bottlene…

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