This work is also the first effort to benchmark the stability of causal discovery algorithms with respect to the values of their hyperparameters.

Understanding long text is of great demands in practice but beyond the reach of most language-image pre-training (LIP) models.

Inthis work, we show that the reason for this deficiency is that language models are biased to learn word co-occurrence statistics instead of true factual associations.

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings.

Embeddings play a pivotal role across various disciplines, offering compact representations of complex data structures.

Recent methodologies in LLM self-training mostly rely on LLM generating responses and filtering those with correct output answers as training data. This approach often yields a low-quality fine-tuning training set (e.g., incorrect plans or intermediate reasoning).

This paper reveals that the common downstream practice of taking a dot product is only a zeroth-order approximation of the optimization goal, resulting in a loss of information during test-time.