arXiv:2605.31056v1 Announce Type: new Abstract: Zero Pronouns (ZPs) are a pervasive linguistic phenomenon in pro-drop languages such as Chinese and have long posed a challenge for natural language processing systems. Although Large Language Models (LLMs) perform well on many Chinese language tasks, their ability to process ZPs remains poorly understood. We conduct a systematic investigation of LLMs' handling of Chinese ZPs through a sequence of linguistically motivated tasks, including identification, referentiality classification, referential type classification, resolution, and translation.
The proliferation of Large Language Models has intensified research into their specific linguistic capabilities and limitations, particularly in complex non-English languages like Chinese.
This research provides critical insights into the performance and developmental needs of LLMs for nuanced language understanding, which is essential for global AI applications and market penetration.
Understanding of LLM limitations in handling challenging linguistic phenomena like zero pronouns in Chinese is refined, highlighting areas for targeted improvement and benchmarking.
- · AI researchers and developers
- · Companies building Chinese NLP applications
- · Users of Chinese language AI tools
- · Developers of generic LLMs without strong Chinese linguistic depth
Improved performance of LLMs on Chinese language tasks requiring sophisticated pronoun resolution.
Increased adoption of LLMs in highly specialized Chinese natural language processing domains such as legal or medical translation.
Enhanced global competitiveness for non-English LLM development, potentially reducing reliance on models primarily trained on English datasets.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL