ProMoGen generates human motion conditioned on both a trajectory and sparse anchor postures via a diffusion transformer trained with a dense-to-sparse curriculum.
Multilingual Relative Clause Attachment Ambiguity Resolution in Large Language Models
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abstract
This study examines how large language models (LLMs) resolve relative clause (RC) attachment ambiguities and compares their performance to human sentence processing. Focusing on two linguistic factors, namely the length of RCs and the syntactic position of complex determiner phrases (DPs), we assess whether LLMs can achieve human-like interpretations amid the complexities of language. In this study, we evaluated several LLMs, including Claude, Gemini and Llama, in multiple languages: English, Spanish, French, German, Japanese, and Korean. While these models performed well in Indo-European languages (English, Spanish, French, and German), they encountered difficulties in Asian languages (Japanese and Korean), often defaulting to incorrect English translations. The findings underscore the variability in LLMs' handling of linguistic ambiguities and highlight the need for model improvements, particularly for non-European languages. This research informs future enhancements in LLM design to improve accuracy and human-like processing in diverse linguistic environments.
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cs.CV 1years
2025 1verdicts
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PMG: Progressive Motion Generation via Sparse Anchor Postures Curriculum Learning
ProMoGen generates human motion conditioned on both a trajectory and sparse anchor postures via a diffusion transformer trained with a dense-to-sparse curriculum.