4 accepted papers
With the widespread application of Large Language Models (LLMs) in various tasks, the mainstream LLM platforms generate massive user-model interactions daily. In order to efficiently analyze the performance of models and diagnose failures in their answers, it is essential to develop an automated fra…
Long-term series forecasting is crucial in real-world applications. Existing works have leveraged self-attention mechanisms and multi-scale temporal data to learn complex dependencies and patterns in long-term series prediction. However, most prediction models directly aggregate together multi-scale…
As Large Language Models (LLMs) become more capable of handling increasingly complex tasks, the evaluation set must keep pace with these advancements to ensure it remains sufficiently discriminative. Item Discrimination (ID) theory, which is widely used in educational assessment, measures the abilit…
Recent studies have shown that reinforcement learning (RL) methods have brought significant performance gains for stock portfolio management (PM) because they effectively utilize historical price information and directly generate portfolio weights. We found, however, that there is still great room f…