Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

Recently, there has been a surge of interest in the NLP community on the use of pretrained
Language Models (LMs) as Knowledge Bases (KBs). Researchers have shown that LMs …

Recently, remarkable progress has been made in automated task-solving through the use of
multi-agent driven by large language models (LLMs). However, existing LLM-based multi …

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

Recent work has shown exciting promise in updating large language models with new
memories, so as to replace obsolete information or add specialized knowledge. However …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

We analyze the storage and recall of factual associations in autoregressive transformer
language models, finding evidence that these associations correspond to localized, directly …

This paper presents a case study on the design, administration, post-processing, and
evaluation of surveys on large language models (LLMs). It comprises two components:(1) A …

The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on
prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that …

Pretrained language models (LMs) are susceptible to generate text with nonfactual
information. In this work, we measure and improve the factual accuracy of large-scale LMs …