The pretraining data of today's strongest language models is opaque; in particular,

Additionally, prompt-based FT with the PCP outperforms state-of-the-art semi-supervised approaches with greater simplicity, eliminating the need for an iterative process and extra data augmentation. Our further analysis explores the performance lower bound of the PCP and reveals that the advantages of PCP persist across different sizes of models and datasets.

Existing LVLM benchmarks primarily focus onsingle-choice questions orshort-form responses, which donotadequately assess the capabilities ofLVLMs inreal-world human-AI interaction applications.

Table 6: Results of selected models on Task 1 (Grouping) using contextual embeddings. In this section, we provide additional t-SNE projections of embeddings from various methods used. Figure 7: Solved wall for Task 1 (Grouping) using GloV e. Left: ( " Suspension" is " a term used in musical harmony " in this context. Grief " in the embedding space, which matches the " Good ___! " connection. Figure 8: Solved wall for Task 1 (Grouping) using FastText (Crawl). Left: contextual embedding solved 3/4 groups. Here the clue " Rambrandt" is placed near other Dutch painters. Right: static embedding solved 0/4 groups. The following section provides answers to questions listed in datasheets for datasets. For what purpose was the dataset created? Was there a specific task in mind? Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., The dataset has been collectively curated by the authors of this paper. What support was needed to make this dataset?