Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

With the rise of Large Language Models (LLMs) in recent years, new opportunities are
emerging, but also new challenges, and contamination is quickly becoming critical …

Chunking information is a key step in Retrieval Augmented Generation (RAG). Current
research primarily centers on paragraph-level chunking. This approach treats all texts as …

LLMs and RAG systems are now capable of handling millions of input tokens or more.
However, evaluating the output quality of such systems on long-context tasks remains …

We present chain-of-knowledge (CoK), a novel framework that augments large language
models (LLMs) by dynamically incorporating grounding information from heterogeneous …

The emergence of unveiling human-like behaviors in Large Language Models (LLMs) has
led to a closer connection between NLP and human psychology. Scholars have been …

Since their inception in the 1980s, language models (LMs) have been around for more than
four decades as a means for statistically modeling the properties observed from natural …

Large Language Models (LLMs), based on their highly developed ability to comprehend and
generate human-like text, promise to revolutionize all aspects of society. These LLMs …

Purpose Following the recent rise in generative artificial intelligence (GenAI) tools,
fundamental questions about their wider impacts have started to reverberate around various …

Data extraction is a crucial, yet labor‐intensive and error‐prone part of evidence synthesis.
To date, efforts to harness machine learning for enhancing efficiency of the data extraction …