Text-to-SQL Error Correction with Language Models of Code

Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing that token-level edits are out of context and sometimes ambiguous, we propose building clause-level edit models instead. Besides, while most language models of code are not specifically pre-trained for SQL, they know common data structures and their operations in programming languages such as Python. Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code. Our error correction model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. Our code and data are available at https://github.com/OSU-NLP-Group/Auto-SQL-Correction.Comment: ACL 2023 Short Pape

Influence of Financialization of Heavily Polluting Enterprises on Technological Innovation under the Background of Environmental Pollution Control

In the wake of the acceleration of China’s industrialization and rapid economic growth, environmental pollution has also attracted great attention. The technological innovation of heavily polluting enterprises is conducive to reducing pollution emissions and promoting environmental health. The financial investment tendency and behavior of real enterprises have a significant impact on the technological innovation decision-making of enterprises. A panel model is used in this paper in order to empirically test the impact of financialization of Chinese heavily polluting enterprises on technological innovation based on the data of Listed Companies in Chinese heavily polluting industries from 2008 to 2019. The + results show that the financialization of heavily polluting enterprises has a significant crowding out effect on technological innovation. After introducing arbitrage motivation as the regulating variable, further research finds that arbitrage motivation weakens the inhibitory effect of enterprise financialization on technological innovation, that is, the stronger the arbitrage motivation, the smaller the negative effect of financialization on enterprise technological innovation, which weakens this crowding out effect. Finally, the listed enterprises in heavily polluting industries are divided into state-owned enterprises and non-state-owned enterprises according to their corporate attributes. Compared with state-owned enterprises, the financialization of non-state-owned enterprises has a greater squeeze out of technological innovation; and arbitrage motivation has a more significant regulatory effect on the impact of enterprise financialization on technological innovation

Automatic Evaluation of Attribution by Large Language Models

A recent focus of large language model (LLM) development, as exemplified by generative search engines, is to incorporate external references to generate and support their claims. However, evaluating the attribution, i.e., verifying whether the generated statement is indeed fully supported by the cited reference, remains an open problem. Although human evaluation is common practice, it is costly and time-consuming. In this paper, we investigate the automatic evaluation of attribution by LLMs. We begin by providing a definition of attribution and then explore two approaches for automatic evaluation: prompting LLMs and fine-tuning smaller LMs. The fine-tuning data is repurposed from related tasks, such as question answering, fact-checking, natural language inference, and summarization. To facilitate the evaluation, we manually curate a set of test examples covering 12 domains from a generative search engine, New Bing. Our results on the curated test set and simulated test examples from existing benchmark questions highlight both promising signals as well as remaining challenges for the automatic evaluation of attribution. We hope our testbed, modeling methodology, and insights will help lay the foundation for future studies on this important problem

Effect of Long-Term Fertilization on Aggregate Size Distribution and Nutrient Accumulation in Aeolian Sandy Soil

Soil aggregates are the material basis of soil structure and important carriers of nutrients. Long-term application of organic and inorganic fertilizers can affect the composition of soil aggregates to varying degrees, which in turn affects the distribution and storage of soil nutrients. We report the results of a 15-year long-term field-based test of aeolian sandy soil and used the wet sieve method to analyze the stability of water-stable aggregates, as well as the distribution characteristics of nutrients in different particle size aggregates. Our results show that long-term application of organic fertilizer (M3) and combined organic–inorganic treatments (NPK1-M1, NPK1-M2, and NPK1-M3) help to increase the amount of organic carbon, inorganic carbon, and cation exchange in the macro-aggregates, and the improvement rates are 92–103%, 8–28%, and 74–85%, respectively. The organic content of the fertilizers also promotes the formation of macro-aggregates, and the stability of aggregates increase from 0.24 to 0.45. In contrast, the application of inorganic fertilizers (NPK1, NPK2, and NPK3) has no marked effect on the formation and stability of macro-aggregates; the application of inorganic fertilizers can merely maintain the organic carbon content of the soil. Correlation analysis shows that the application of organic fertilizers and chemical (inorganic) fertilizers containing phosphorus and potassium can markedly increase the content and reserves of available phosphorus and potassium across all aggregate sizes, and there is a significant positive correlation between these parameters and the amount of applied fertilizer (p < 0.05). Aggregates of various sizes in aeolian sandy soils in arid areas have the potential for greater nutrient storage. Therefore, organic fertilizers can be used in the agricultural production process to improve soil structure and fertility

Evaluation of developmental toxicity of safinamide in zebrafish larvae (Danio rerio)

Monoamine oxidase-B (MAO-B), as a principal metabolizing enzyme, plays important roles in the metabolism of catecholamines and xenobiotics in the central nervous system and peripheral tissues. Safinamide, the third-generation reversible MAO-B inhibitor, has potential to alleviate many neurological diseases such as Parkinson's disease (PD) and depression. Exposure to clinical psychotropic drugs often has adverse effects on fetuses. Currently, a variety of studies of safinamide focus on its curative effect and pharmacological effect, while its side effect of embryonic development is barely studied. In this study, we used zebrafish as a model to evaluate the embryonic developmental toxicity of safinamide. Our results revealed that higher concentrations (30 μM) of safinamide treatment caused a decrease in hatching rate and an increase in malformation and mortality in zebrafish larvae. Meanwhile, we observed that lower safinamide exposure (10 μM) increased the body length of zebrafish larvae and resulted in hyperactivity-like behaviors. In addition, an increased trend in dopamine (DA) level was found in 3.3 μM and 10 μM safinamide-exposed groups. Transcriptome analysis identified that safinamide exposure may disturb a variety of physiological processes such as neuroactive ligand-receptor interaction signaling pathway. In summary, our study reveals that safinamide may cause developmental defects in zebrafish larvae and provides insights into its toxic reactions in early develoment