Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task

Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some studies indicated that large language models fail to achieve promising result beyond the state-of-the-art models in English grammatical error correction (GEC) tasks. In this report, we aim to explore the how large language models perform on Chinese grammatical error correction tasks and provide guidance for future work. We conduct experiments with 3 different LLMs of different model scale on 4 Chinese GEC dataset. Our experimental results indicate that the performances of LLMs on automatic evaluation metrics falls short of the previous sota models because of the problem of over-correction. Furthermore, we also discover notable variations in the performance of LLMs when evaluated on different data distributions. Our findings demonstrates that further investigation is required for the application of LLMs on Chinese GEC task

A Bubble-Assisted Approach for Patterning Nanoscale Molecular Aggregates

We demonstrate a new approach to pattern functional organic molecules with a template of foams, and achieve a resolution of sub 100 nm. The bubble-assisted assembly (BAA) process is consisted of two periods, including bubble evolution and molecular assembly, which are dominated by the Laplace pressure and molecular interactions, respectively. Using TPPS (meso-tetra(4-sulfonatophenyl) porphyrin), we systematically investigate the patterns and assembly behaviour in the bubble system with a series of characterizations, which show good uniformity in nanoscale resolution. Theoretical simulations reveal that TPPS's J-aggregates contribute to the ordered construction of molecular patterns. Finally, we propose an empirical rule for molecular patterning approach, that the surfactant and functional molecules should have the same type of charge in a two-component system. This approach exhibits promising feasibility to assemble molecular patterns at nanoscale resolution for micro/nano functional devices