The keywords were extracted using the procedure described in SectionC. The restricted part of the Muharaf dataset has 428 images distributed under a proprietary license.

One of the fundamental cognitive abilities of humans is to quickly resolve uncertainty by generating hypotheses and testing them via active trials.

Text-to-SQL parsing, which aims at converting natural language questions into executable SQLs, has gained increasing attention in recent years.

Q: For what purpose was the dataset created? Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Q: Who funded the creation of the dataset? Q: What do the instances that comprise the dataset represent (e.g., documents, photos, people, Q: How many instances are there in total (of each type, if appropriate)? As shown in Table 1, the dataset statistics are as follows: Grounding Task: 111,770 samples for training, 21,616 samples for testing. For grounding, we use only one annotation per image.

Large Language Models (MLLMs) to follow task instructions and improve generalization ability.

How to evaluate Large Language Models (LLMs) in code generation remains an open question.

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

We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research.