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Lost in Translation: Repurposing semantic similarity benchmarks for evaluating lexical-semantic consistency in LLM-based machine translation

We propose and demonstrate a repurposing of the lexical similarity benchmark Multi-SimLex and the SimLex-999 family of resources for assessing the cross-lingual lexical-semantic consistency of multilingual large language models. While originally gathered for evaluating word embedding models, the parallel nature of the word pairs enables their use in machine translation settings. Using a manually verified subset of 500 word pairs from the Multi-SimLex dataset, we evaluate models’ ability to assess semantic similarity and perform translation between English and Mandarin through zero-shot prompting. We compare BLOOMZ and GPT-4’s similarity ratings against human-annotated benchmarks and examine translation consistency using our and other metrics, with GPT-4 showing stronger human alignment. As SimLex-999 and Multi-SimLex together cover a range of at least 25 languages, this approach has the potential to be extended to many language pairs including ones that don’t involve English, though it requires some manual checks.