Diagnosing Hallucination Risk in AI Surgical Decision-Support: A Sequential Framework for Sequential Validation

LLMSQL has been introduced as an upgraded version of WikiSQL, addressing various structural and annotation issues that have hindered its effectiveness in converting natural language questions into SQL queries. This systematic revision aims to enhance the interaction of non-expert users with relational databases in the context of large language models (LLMs).

Recent empirical studies have explored the capabilities of slow-thinking large language models (LLMs) like DeepSeek-R1 and ChatGPT-o1 in time series forecasting (TSF), proposing a new framework called TimeReasoner that treats TSF as a conditional reasoning task. This approach aims to enhance the models' ability to reason over temporal patterns, potentially improving forecasting accuracy even in zero-shot scenarios.

Recent research indicates that large language models (LLMs) can enhance their reasoning capabilities through pure reinforcement learning (RL) focused on problem-solving, without the need for process reward models (PRMs). This finding challenges the traditional belief that PRMs are essential for developing reasoning skills in LLMs, as demonstrated by the DeepSeek-R1 model.