Remarks on CLEO New Measurements for Upsilon(1S) Decays to Charmonium Final States and Investigations on Associate Strange Particle Enhancement in Upsilon to J/Psi +X

The recent measurements by CLEO Collaboration for the inclusive J/Psi and Psi(2S) production in Upsilon(1S) decay and our previous calculation are analyzed. The J/Psi momentum spectrum and the production ratio of Psi(2S) versus J/Psi favour Upsilon to J/Psi(Psi(2S)) + ccbar g as the dominant contribution. We point out that the differences between the experimental data and our previous results are mainly originated from the setting of the parameter charm quark mass. We further suggest the associate strange particle enhancement as a probe for the open charm particles in Upsilon to J/Psi (Psi(2S)) + ccbar g.Comment: minor corrections during proof-readin

GPT4Table: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, there is still much to learn about how well LLMs understand structured data, such as tables. While it is true that tables can be used as inputs to LLMs with serialization, there is a lack of comprehensive studies examining whether LLMs can truly comprehend such data. In this paper, we try to understand this by designing a benchmark to evaluate the structural understanding capabilities (SUC) of LLMs. The benchmark we create includes seven tasks, each with its own unique challenges, \eg, cell lookup, row retrieval, and size detection. We conduct a series of evaluations on GPT-3.5 and GPT-4. We find that the performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks. Drawing from the insights gained through the benchmark evaluations, we propose \textit{self-augmentation} for effective structural prompting, such as critical value / range identification using LLMs' internal knowledge. When combined with carefully chosen input choices, these structural prompting methods lead to promising improvements in LLM performance on a variety of tabular tasks, \eg, TabFact($\uparrow2.31\%$), HybridQA($\uparrow2.13\%$), SQA($\uparrow2.72\%$), Feverous($\uparrow0.84\%$), and ToTTo($\uparrow5.68\%$). We believe that our benchmark and proposed prompting methods can serve as a simple yet generic selection for future research.Comment: This paper has been accepted as a full paper at WSDM 202

Boron-mediated directed aromatic C–H hydroxylation

Transition metal-catalysed C–H hydroxylation is one of the most notable advances in synthetic chemistry during the past few decades and it has been widely employed in the preparation of alcohols and phenols. The site-selective hydroxylation of aromatic C–H bonds under mild conditions, especially in the context of substituted (hetero)arenes with diverse functional groups, remains a challenge. Here, we report a general and mild chelation-assisted C–H hydroxylation of (hetero)arenes mediated by boron species without the use of any transition metals. Diverse (hetero)arenes bearing amide directing groups can be utilized for ortho C–H hydroxylation under mild reaction conditions and with broad functional group compatibility. Additionally, this transition metal-free strategy can be extended to synthesize C7 and C4-hydroxylated indoles. By utilizing the present method, the formal synthesis of several phenol intermediates to bioactive molecules is demonstrated