This work presents an interactive Text-to-SQL framework that models SQL generation as probabilistic reasoning over multiple candidate queries and selects clarification questions based on the principle of Expected Information Gain, providing an information-theoretic criterion for prioritizing high-impact clarifications...
Lu-Yu Qiu, Jia-Ning Li, Yuan-Feng Song et al.· Workshop Proceedings of the...· 0 citations
This work constructed Illusion-Reasoning, a benchmark of illusion images collected from the real world, incorporating diverse annotated question-answer pairs, and shows that the reasoning capabilities of a wide range of LVLMs are not as advanced as claimed.
The findings reveal that transformer-based large language models exhibit learning patterns similar to those of human learners, with a faster learning speed for simpler subtasks compared to more complex ones, which suggests that transformer-based LLMs may share cognitive processes with human learners in arithmetic.
Luyu Qiu, Jianing Li, Hwanhee Kim et al.· 0 citations
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