How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions

While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition? This study seeks to explore this question through the lens of syntax, a crucial component of sentence comprehension. Adopting a natural language question-answering (Q&A) scheme, we craft questions targeting nine syntactic knowledge points that are most closely related to sentence comprehension. Experiments conducted on 24 LLMs suggest that most have a limited grasp of syntactic knowledge, exhibiting notable discrepancies across different syntactic knowledge points. In particular, questions involving prepositional phrase attachment pose the greatest challenge, whereas those concerning adjectival modifier and indirect object are relatively easier for LLMs to handle. Furthermore, a case study on the training dynamics of the LLMs reveals that the majority of syntactic knowledge is learned during the initial stages of training, hinting that simply increasing the number of training tokens may not be the `silver bullet' for improving the comprehension ability of LLMs.Comment: 20 pages, 6 figure

Screening and genotyping of Mur blood group among voluntary blood donors in the population of Hezhou, Guangxi

Objective To screen the distribution frequency of Mur blood group among voluntary blood donors in Hezhou, Guangxi, and further analyze the molecular basis of of Mur antigen positive samples. Methods The Mur phenotype of voluntary blood donors in Hezhou was serologically screened using microplate method, and the distribution frequency of Mur antigens in different ethnic groups was analyzed. Genetic typing was performed on these positive samples with PCR-SSP method to verify the accuracy of the serological method, and the genetic background was sequenced and analyzed. Results Among 3 298 samples from voluntary blood donors in Hezhou, 432(13.10%, 432/3 298) were screened positive for Mur antigen, and PCR-SSP genotyping validation showed that all 432 samples were electrophoretic positive. Among them, the proportion of Han blood donors with positive Mur antigen was 12.79%(331/2 587), Yao ethnic group was 13.25%(64/483), Zhuang ethnic group was 16.51%(36/218), and no statistically significant difference was found in the three groups(P>0.05). Further sequencing results showed that 428 samples were GYP(B-A-B) Mur, also known as GYP. Mur type(12.98%, 428/3 298), the other 4 samples were GYP(B-A-B) Bun, also known as GYP. Bun type(0.12%, 4/3 298). Conclusion The Mur blood type frequency is high in the voluntary blood donors in Hezhou, Guangxi, and is predominant characterized by GYP. Mur genotype. Due to ethnic integration, no significant difference was noticed in the frequency of Mur blood type distribution between Han, Zhuang and Yao population. Therefore, conducting extensive Mur blood group antigen and antibody testing in Hezhou is of great significance for ensuring clinical blood transfusion safety