Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

Large language models have achieved impressive performance on various natural
language processing tasks. However, so far they have been evaluated primarily on …

We introduce a new type of test, called a Turing Experiment (TE), for evaluating to what
extent a given language model, such as GPT models, can simulate different aspects of …

This comprehensive review explores the intersection of Large Language Models (LLMs) and
cognitive science, examining similarities and differences between LLMs and human …

Large language models (LLMs) such as ChatGPT have sparked extensive discourse within
the medical education community, spurring both excitement and apprehension. Written from …

Do large language models (LLMs) know the law? LLMs are increasingly being used to
augment legal practice, education, and research, yet their revolutionary potential is …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Large language models such as Codex, have shown the capability to produce code for
many programming tasks. However, the success rate of existing models is low, especially for …

Auditing large language models for unexpected behaviors is critical to preempt catastrophic
deployments, yet remains challenging. In this work, we cast auditing as an optimization …

One widely cited barrier to the adoption of LLMs as proxies for humans in subjective tasks is
their sensitivity to prompt wording—but interestingly, humans also display sensitivities to …