Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendencytoproduce hallucinations, wherein themodels fabricate incorrectstatements on tasks beyond their knowledge and perception.

The ability of large language models (LLMs) to mimic human-like intelligence hasledtoasurgeinLLM-based autonomous agents. ThoughrecentLLMsseem capable of planning and reasoning given user instructions, their effectiveness in applying these capabilities for autonomous task solving remains underexplored. This is especially true in enterprise settings, where automated agents hold the promise of a high impact.

To mitigate the dependency on labeled data and enhance the resilience of graph models, selfsupervised learning has been widely adopted in GNN training.

Sparsified Online Newton (SONew) method, a memory-efficient second-order algorithm that yields a sparsified yet effective preconditioner.

Large language models (LLMs) have achieved a remarkable performance on diverse tasks across multiple domains, as reported in recent surveys [Wei et al., 2022, Bubeck et al., 2023].

Videos introduce intrinsic complexities inboth spatial and temporal dimensions, whichare not present in staticimages.

We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective.

Since some brand-new methods utilize post-processing, such as calibration stacking [5] or domain detector [2, 12], to alleviate the domain shift problem, we report the results of our Dual Progressive Prototype Network (DPPN) with post-processing in Table 3 of the main paper for fair comparison. In this part, we further compare our DPPN with recent methods that clearly report their results without post-processing, of which the comparison results are shown in Table 1. APN [15] only reports their results with calibration stacking. Our DPPN outperforms the best one by respectively 15. 3%, 8. 8%, and 7. 3% for H on CUB, AWA2, and aPY datasets, and obtains comparable performance on SUN dataset. This demonstrates the effectiveness of learning representations that progressively explore category discrimination and attribute-region correspondence.