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Found 5 papers, 1 papers with code
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research.
Multi-Hop Table Retrieval for Open-Domain Text-to-SQL
To reduce the effect of the similar irrelevant entity, our method focuses on unretrieved entities at each hop and considers the low-ranked tables by beam search.
Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes
Numerical reasoning is an essential ability for NLP systems to handle numeric information.
A Survey of Table Reasoning with Large Language Models
In this paper, we analyze the mainstream techniques used to improve table reasoning performance in the LLM era, and the advantages of LLMs compared to pre-LLMs for solving table reasoning.
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
