Elasticsearch (ES) is a distributed RESTful search engine optimized for large-scale and long-text search scenarios. Recent research on text-to-Query has explored using large language models (LLMs) to convert user query intent to executable code, making it an increasingly popular research topic. To our knowledge, we are the first to introduce the novel semantic parsing task text-to-ES. To bridge the gap between LLM and ES, in detail, we leverage LLMs and employ domain experts to generate ES query bodies, which are Domain-Specific Language (DSL), along with the corresponding post-processing code to support multi-index ES queries. Consequently, we propose the text-to-ES benchmark that consists of two datasets: Large Elasticsearch Dataset (LED), containing 26,207 text-ES pairs derived from a 224.9GB schema-free database, and ElasticSearch (BirdES)with 10,926 pairs sourced from the Bird dataset on a 33.4GB schema-fixed database. Compared with fourteen advanced LLMs and six code-based LLMs, the model we trained outperformed DeepSeek-R1 by 15.64% on the LED dataset, setting a new state-of-the-art, and achieved 78% of DeepSeek-R1’s performance on the BirdES dataset. Additionally, we provide in-depth experimental analyses and suggest future research directions for this task. Our datasets are available at https://huggingface.co/datasets/Barry1915/Text-to-ES.

Few-shot table-to-text generation seeks to generate natural language descriptions for the given table in low-resource scenarios. Previous works mostly utilized Pre-trained Language Models (PLMs) even Large Language Models (LLMs) to generate fluent descriptions of the tables. However, they are prone to hallucinations that do not conform to the table. In this work, we propose CoT-Planner, a simple but efficient Chain-of-Thoughts-based approach that can be used to reduce the generation of hallucinations in the few-shot table-to-text generation. We first use a LLM (such as ChatGPT) to automatically generate ten intermediate content plans in the form of a Chain-of-Thoughts (CoT) for each table and corresponding description pair. Then, we refined the most accurate content plan for each sample and used the table and text pairs with the added content plan (CoT-Plan) as demonstrations for In-Context Learning (ICL). Both automatic and human evaluations on the numericNLG dataset show our method can effectively alleviate hallucinations, thereby improving factual consistency in few-shot table-to-text generation. The code and data can be accessed from https://github.com/FXLP/CoT-Planner.