EICopilot uses LLM agents with in-context learning and entity masking to generate Gremlin queries for enterprise knowledge graph search, reporting better accuracy than zero-shot baselines on 150 internal queries.
Content Enhanced BERT-based Text-to-SQL Generation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem. Based on the observation that some of the table content match some words in question string and some of the table header also match some words in question string, we encode two addition feature vector for the deep model. Our methods also benefit the model inference in testing time as the tables are almost the same in training and testing time. We test our model on the WikiSQL dataset and outperform the BERT-based baseline by 3.7% in logic form and 3.7% in execution accuracy and achieve state-of-the-art.
citation-role summary
background 1
citation-polarity summary
fields
cs.IR 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
EICopilot: Search and Explore Enterprise Information over Large-scale Knowledge Graphs with LLM-driven Agents
EICopilot uses LLM agents with in-context learning and entity masking to generate Gremlin queries for enterprise knowledge graph search, reporting better accuracy than zero-shot baselines on 150 internal queries.