MoT: Memory-of-Thought Enables ChatGPT to Self-Improve

Large Language Models (LLMs) have shown impressive abilities in various tasks. However, fundamentally improving them depends on high-quality datasets or computationally expensive fine-tuning. On the contrary, humans can easily improve themselves by self-thinking and memory, without external resources. In this paper, we propose a framework, MoT, to let the LLM self-improve through Memory-of-Thought, without annotated datasets and parameter updates. Specifically, MoT is divided into two stages: 1. before the test stage, the LLM pre-thinks on the unlabeled dataset and saves the high-confidence thoughts as external memory; 2. During the test stage, given a test question, the LLM recalls relevant memory to help itself reason and answer it. Experimental results show that MoT can help ChatGPT significantly improve its abilities in arithmetic reasoning, commonsense reasoning, factual reasoning, and natural language inference. Further analyses show that each component contributes critically to the improvements and MoT can lead to consistent improvements across various CoT methods and LLMs.Comment: Accepted to appear at EMNLP 202

Finding Support Examples for In-Context Learning

Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible. Hence we propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages: First we filter the dataset to obtain informative in-context examples individually. Specifically, we propose a novel metric, InfoScore, to evaluate the example's in-context informativeness based on the language model's feedback, and further propose a progressive filtering process to filter out uninformative examples. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations, to find examples that fully depict the task. The experimental results show that LENS significantly outperforms a wide range of baselines.Comment: Accepted to the Findings of EMNLP 202

Study on the Influence of Ultrasonic Vibration on the Specific Energy of Sawing Ceramic

AbstractThe hard as well as brittle constituents are typically difficult-to-machined materials, and this character upsurges the machining cost. Many non-traditional machining methods were developed to improve its cost-effectiveness. Ultrasonic vibration assisted grinding has been improved the processing performance of a variety of brittle materials, and achieved good results in processing application. In this study, engineering ceramic was precisely sawn using a thin diamond blade with or without ultrasonic vibration conditions. During the sawing process, the specific sawing energy was investigated with the measurement of sawing forces to explore the influence of ultrasonic vibration. The results showed that the ultrasonic vibration made a significant reduction in specific sawing energy. The specific sawing energy decreased with the increase of the maximum undeformed chip thickness in both the sawing conditions; however ultrasonic vibration changed the trend of specific sawing energy in normal cutting mode from exponentially decreasing to a good linear decreasing. Under the ultrasonic vibration assisted sawing condition, the impact of the diamond grain on the engineering ceramic caused to much more material removal in brittle fracture mode. The reducing of the plastic transformation also reduced the energy consumption during the engineering ceramic sawing process

Perceptions of Heroism: Characteristics, Functions and Influencing Factors among Chinese College Students in the Post-pandemic Era

Heroes play a significant role in shaping the popular perceptions of morality, justice, and social values in general. During the Covid-19 pandemic, people’s anticipation for heroes doubles and their heroism may be reshaped by the pandemic. This paper attempts to investigate the perceived heroism of Chinese higher education students(n=847) in the post-pandemic era by means of the online questionnaire. Firstly, we explore the main characteristics of heroes worshipped by Chinese higher education students, which are summarized as diversified, epoch-making and civilian. Then we investigate the functions of heroes, which are categorized as enhancing, moral modeling and protecting. Finally, we analyze the five factors (intrinsic attraction, social reinforcement, education, family background and publicity) that may predict students’ heroism worship. As the regression analysis reveals, the five factors have significantly positive influences on higher education students’ perceptions of heroism and the weights of intrinsic attraction, social reinforcement, publicity, family background and education are 0.364, 0.316, 0.227, 0.190 and 0.156 respectively. These findings not only provide a theoretical and empirical contribution to the study of heroism, but also help develop Chinese higher education sustainable development in the post-pandemic era

The impact of the collaborative innovation network embeddedness on enterprise green innovation performance

As the market environment becomes increasingly competitive, enterprises that rely solely on internal research and development innovation are no longer sufficient to meet the demands of competition. Consequently, enterprises have broken down organizational boundaries and shifted from closed innovation to open collaborative innovation. The flow of knowledge across organizations facilitates the acquisition of heterogeneous resources from partners, promotes the integration and configuration of internal and external knowledge, thereby enhances the competitiveness of enterprises. However, some scholars argued that collaborative innovation does not always achieve win-win outcomes, and the existence of substitution effects between enterprises and their partners may hinder the innovation level of the focal enterprise. Therefore, based on the resource-based theory and the network embeddedness theory, this study constructs a theoretical model to investigate the effects of network embeddedness, network experience and partner diversity on enterprise green innovation performance in the Chinese green collaborative innovation network. The impact of network embeddedness on innovation performance is examined from two dimensions: structural embeddedness and relational embeddedness. The moderating effects of network experience and partner diversity are analyzed to further explain this phenomenon. Using Chinese green patent data from 2000 to 2015 as the research object, the collaborative innovation network of enterprises is constructed, and the network characteristic variables are calculated using social network analysis methods. Finally, negative binomial regression and robustness tests are conducted using STATA software. The research findings provide managerial implications for Chinese enterprises to achieve competitiveness and sustainable development