Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

Evaluating large language model (LLM) based chat assistants is challenging due to their
broad capabilities and the inadequacy of existing benchmarks in measuring human …

Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

This article presents a comprehensive and practical guide for practitioners and end-users
working with Large Language Models (LLMs) in their downstream Natural Language …

We introduce a method to measure uncertainty in large language models. For tasks like
question answering, it is essential to know when we can trust the natural language outputs …

Instruction-following models are attractive alternatives to fine-tuned approaches for question
answering (QA). By simply prepending relevant documents and an instruction to their input …

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Instruction tuning large language models (LLMs) remains a challenging task, owing to the
complexity of hyperparameter selection and the difficulty involved in evaluating the tuned …

Numerous benchmarks have been established to assess the performance of foundation
models on open-ended question answering, which serves as a comprehensive test of a …