Unsupervised multitask pre-training has been the critical method behind the recent success
of language models (LMs). However, supervised multitask learning still holds significant …

The success of Large Language Models (LLMs) is inherently linked to the availability of vast,
diverse, and high-quality data for training and evaluation. However, the growth rate of high …

This survey paper investigates how personalized learning offered by Large Language
Models (LLMs) could transform educational experiences. We explore Knowledge Editing …

It is often desirable to distill the capabilities of large language models (LLMs) into smaller
student models due to compute and memory constraints. One way to do this for classification …

Teaching large language models (LLMs) to generate text with citations to evidence sources
can mitigate hallucinations and enhance verifiability in information-seeking systems …

The programming skill is one crucial ability for Large Language Models (LLMs),
necessitating a deep understanding of programming languages (PLs) and their correlation …

Large language models (LLMs) have shown remarkable capability in numerous tasks and
applications. However, fine-tuning LLMs using high-quality datasets under external …

Data scarcity in low-resource languages can be addressed with word-to-word translations
from labeled task data in high-resource languages using bilingual lexicons. However …

We introduce Bonito, an open-source model for conditional task generation that converts
unannotated text into task-specific training datasets for instruction tuning. We aim to enable …

Instruction tuning has been identified as a crucial technique for optimizing the performance
of large language models (LLMs) in generating human-aligned responses. Nonetheless …