Tootfinder

NL2KQL: From Natural Language to Kusto Query
Amir H. Abdi, Xinye Tang, Jeremias Eichelbaum, Mahan Das, Alex Klein, Nihal Irmak Pakis, William Blum, Daniel L Mace, Tanvi Raja, Namrata Padmanabhan, Ye Xing
https://arxiv.org/abs/2404.02933

NL2KQL: From Natural Language to Kusto Query
Data is growing rapidly in volume and complexity. Proficiency in database query languages is pivotal for crafting effective queries. As coding assistants become more prevalent, there is significant opportunity to enhance database query languages. The Kusto Query Language (KQL) is a widely used query language for large semi-structured data such as logs, telemetries, and time-series for big data analytics platforms. This paper introduces NL2KQL an innovative framework that uses large language mod…

Symmetry Resolution in non-Lorentzian Field Theories
Aritra Banerjee, Rudranil Basu, Arpan Bhattacharyya, Nilachal Chakrabarti
https://arxiv.org/abs/2404.02206

Symmetry Resolution in non-Lorentzian Field Theories
Starting from the computation of Symmetry Resolved Entanglement Entropy (SREE) for boosted intervals in a two dimensional Conformal Field Theory, we compute the same in various non-Lorentzian limits, viz, Galilean and Carrollian Conformal Field Theory in same number of dimensions. We approach the problem both from a limiting perspective and by using intrinsic symmetries of respective non-Lorentzian conformal algebras. In particular, we calculate the leading order terms, logarithmic terms, and t…

Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies
Zheng Ning, Yuan Tian, Zheng Zhang, Tianyi Zhang, Toby Li
https://arxiv.org/abs/2402.07304

Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies
Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant improvements in performance, with the best model achieving ~85% percent accuracy on the benchmark Spider dataset. However, there is a lack of a systematic understanding of the types, causes, and effectiveness of error-handling mechanisms of errors for…

This https://arxiv.org/abs/2404.02933 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

NL2KQL: From Natural Language to Kusto Query
Data is growing rapidly in volume and complexity. Proficiency in database query languages is pivotal for crafting effective queries. As coding assistants become more prevalent, there is significant opportunity to enhance database query languages. The Kusto Query Language (KQL) is a widely used query language for large semi-structured data such as logs, telemetries, and time-series for big data analytics platforms. This paper introduces NL2KQL an innovative framework that uses large language mod…