Lexical diversity, the proportion of unique lemmas in a text, is known to vary across first language and second language production. It has been used by researchers as a proxy for language proficiency, particularly for classroom language learners. Lexical diversity has been found to vary by language background, potentially at least partly due to cross-linguistic influence. We investigated whether or how lexical diversity differs across groups with varied language backgrounds, namely monolinguals, bilinguals, adult language learners, and post-attrition speakers. We expected that these groups would show differing mean lexical diversity scores due to their different backgrounds with varying combinations of languages and levels of proficiency. Data from 114 participants were stratified and showed substantial between-group overlap in their lexical diversity scores. However, some significant by-group differences in lexical diversity were found. We discuss the implications of these results.

Despite the widespread adoption of public-facing large language models (LLMs) over the past several months, we still know little about the complexities of machine-generated language in comparison to human-generated language. To better understand how lexical complexity differs between human- and LLM-produced texts, we elicited responses from four commercially-available LLMs (ChatGPT 3.5, ChatGPT 4.0, Claude, and Bard), and compared them to writing from humans from different backgrounds (i.e., L1 and L2 English users) and education levels. We also investigated whether the LLMs demonstrated consistent style across targeted prompts, as compared to the human participants. Through an analysis of six dimensions of lexical diversity (volume, abundance, variety-repetition, evenness, disparity, dispersion), preliminary results suggest that LLM-generated text differs from human-generated with regards to lexical diversity, and texts created by LLMs demonstrate less variation than human-written text. We will discuss the implications of these differences for future research and education in applied linguistics.

Previous studies have found that bilingual children receive different Give-Number task knower-levels across their languages as subset-knowers. However, recent work reveals that the reliability of Give-Number task is moderate for subset-knower levels. This raises the possibility that the differences found in knower-levels across bilinguals’ languages are explained by a lack of reliability of Give-Number. To address this possibility, we presented bilingual children with Give-Number and a subitizing task that they had to perform in both languages. If differences in knower-levels are due to true differences in children’s understanding of small numbers and not random noise, then differences in subitizing abilities should also be non-random. Data collection is still in progress, but preliminary planned analyses (N=13/64) revealed no differences in subitizing abilities across languages, based on knower-levels classification. We discuss implications of these findings on knowledge transfer across bilinguals’ languages.