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 …

Abstract Large Language Models (LLMs) have recently gained significant attention due to
their remarkable capabilities in performing diverse tasks across various domains. However …

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 …

Recent advances in LLMs have led to an abundance of evaluation benchmarks, which
typically rely on a single instruction template per task. We create a large-scale collection of …

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 …

Due to the cumbersome nature of human evaluation and limitations of code-based
evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in …

Interpretable machine learning has exploded as an area of interest over the last decade,
sparked by the rise of increasingly large datasets and deep neural networks …

Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

Structured generation, the process of producing content in standardized formats like JSON
and XML, is widely utilized in real-world applications to extract key output information from …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …