LawBench: Benchmarking Legal Knowledge of Large Language Models

Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is unclear how much legal knowledge they possess and whether they can reliably perform legal-related tasks. To address this gap, we propose a comprehensive evaluation benchmark LawBench. LawBench has been meticulously crafted to have precise assessment of the LLMs' legal capabilities from three cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize needed legal concepts, articles and facts; (2) Legal knowledge understanding: whether LLMs can comprehend entities, events and relationships within legal text; (3) Legal knowledge applying: whether LLMs can properly utilize their legal knowledge and make necessary reasoning steps to solve realistic legal tasks. LawBench contains 20 diverse tasks covering 5 task types: single-label classification (SLC), multi-label classification (MLC), regression, extraction and generation. We perform extensive evaluations of 51 LLMs on LawBench, including 20 multilingual LLMs, 22 Chinese-oriented LLMs and 9 legal specific LLMs. The results show that GPT-4 remains the best-performing LLM in the legal domain, surpassing the others by a significant margin. While fine-tuning LLMs on legal specific text brings certain improvements, we are still a long way from obtaining usable and reliable LLMs in legal tasks. All data, model predictions and evaluation code are released in https://github.com/open-compass/LawBench/. We hope this benchmark provides in-depth understanding of the LLMs' domain-specified capabilities and speed up the development of LLMs in the legal domain

Transcriptome profiling and gene expression analyses of eggplant (Solanum melongena L.) under heat stress.

Global warming induces heat stress in eggplant, seriously affecting its quality and yield. The response to heat stress is a complex regulatory process; however, the exact mechanism in eggplant is unknown. We analyzed the transcriptome of eggplant under different high-temperature treatments using RNA-Seq technology. Three libraries treated at high temperatures were generated and sequenced. There were 40,733,667, 40,833,852, and 40,301,285 clean reads with 83.98%, 79.69%, and 84.42% of sequences mapped to the eggplant reference genome in groups exposed to 28°C (CK), 38°C (T38), and 43°C (T43), respectively. There were 3,067 and 1,456 DEGs in T38 vs CK and T43 vs CK groups, respectively. In these two DEG groups, 315 and 342 genes were up- and down-regulated, respectively, in common. Differential expression patterns of DEGs in antioxidant enzyme systems, detoxication, phytohormones, and transcription factors under heat stress were investigated. We screened heat stress-related genes for further validation by qRT-PCR. Regulation mechanisms may differ under different temperature treatments, in which heat shock proteins and heat stress transcription factors play vital roles. These results provide insight into the molecular mechanisms of the heat stress response in eggplant and may be useful in crop breeding

Self-assembly of gold nanowires along carbon nanotubes for ultrahigh-aspect-ratio hybrids

We report a novel approach for the assembly of one-dimensional hybrid nanostructures that consist of gold nanowires with ultrahigh aspect ratios (L/d &gt; 500) self-assembled along the axes of multiwalled carbon nanotubes. The micrometer-long hybrid nanowires exhibit high electrical conductivity and can be easily microcontact-printed onto various substrates in a patterned form, suggesting that these hybrids have considerable potential as interconnects for nanoelectronic applications.<br /