3 accepted papers
Recent advancements in large language models (LLMs) have highlighted the importance of improving their reasoning capabilities. A critical challenge lies in the scarcity of high-quality reasoning data—characterized by diversity and rich supervisory signals—necessary for robust model training. While d…
Table question answering (TQA) requires accurate retrieval and reasoning over tabular data. Existing approaches attempt to retrieve query-relevant content before leveraging large language models (LLMs) to reason over long tables. However, these methods often fail to accurately retrieve contextually
Although the rise of large language models (LLMs) has introduced new opportunities for time series forecasting, existing LLM-based solutions require excessive training and exhibit limited transferability. In view of these challenges, we propose TimeRAG, a framework that incorporates Retrieval-Augmen…