Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

These features are then fed into a V alue Classification Network (VCN) which directly generates classification solutions.

Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

To address the above issues, we propose CrossGNN, a linear complexity GNN model to refine the cross-scale and cross-variable interaction for MTS. To deal with the unexpected noise in time dimension, an adaptive multi-scale identifier (AMSI) is leveraged to construct multi-scale time series with reduced noise.

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.