多语言大语言模型在双语词处理中难以建立拼写与语义的联系
Multilingual LLMs Struggle to Link Orthography and Semantics in Bilingual Word Processing
AI总结：
研究探讨多语言大语言模型在处理双语词时，如何通过拼写和语义特征进行区分，发现模型在处理双语同形词时存在显著困难，倾向于依赖拼写相似性而非语义理解。
摘要：
双语词汇处理受到两种语言的语音、拼写和语义特征复杂交互的影响。在人类中，这体现在同源词（拼写和意义相似的词，如英语和德语中的'blind'）比跨语言同形词（拼写相同但意义不同，如英语的'gift'和德语的'poison'）处理更易。我们研究多语言大语言模型（LLMs）如何处理此类现象，重点考察英语-西班牙语、英语-法语和英语-德语的同源词、非同源词和跨语言同形词。具体而言，我们评估其在孤立呈现或句子上下文中的歧义消除和语义判断能力。研究发现，尽管某些LLMs在孤立识别同源词和非同源词时表现良好，但在处理跨语言同形词时却表现出显著困难，往往低于随机基线。这表明LLMs在处理跨语言同形词时倾向于依赖拼写相似性而非语义理解。此外，我们发现LLMs在检索词义时存在困难，孤立歧义消除任务的表现与语义理解无相关性。最后，我们研究了LLMs如何处理不协调句子中的跨语言同形词。发现模型在理解英语和非英语同形词时采用不同的策略，突显了缺乏统一处理跨语言歧义的方法。
英文摘要：
Bilingual lexical processing is shaped by the complex interplay of phonological, orthographic, and semantic features of two languages within an integrated mental lexicon. In humans, this is evident in the ease with which cognate words - words similar in both orthographic form and meaning (e.g., blind, meaning "sightless" in both English and German) - are processed, compared to the challenges posed by interlingual homographs, which share orthographic form but differ in meaning (e.g., gift, meaning "present" in English but "poison" in German). We investigate how multilingual Large Language Models (LLMs) handle such phenomena, focusing on English-Spanish, English-French, and English-German cognates, non-cognate, and interlingual homographs. Specifically, we evaluate their ability to disambiguate meanings and make semantic judgments, both when these word types are presented in isolation or within sentence contexts. Our findings reveal that while certain LLMs demonstrate strong performance in recognizing cognates and non-cognates in isolation, they exhibit significant difficulty in disambiguating interlingual homographs, often performing below random baselines. This suggests LLMs tend to rely heavily on orthographic similarities rather than semantic understanding when interpreting interlingual homographs. Further, we find LLMs exhibit difficulty in retrieving word meanings, with performance in isolative disambiguation tasks having no correlation with semantic understanding. Finally, we study how the LLM processes interlingual homographs in incongruent sentences. We find models to opt for different strategies in understanding English and non-English homographs, highlighting a lack of a unified approach to handling cross-lingual ambiguities.
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