The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …

Large language models (LLMs) can be used to serve as agents to simulate human
behaviors, given the powerful ability to understand human instructions and provide high …

Recent breakthroughs in large language models (LLMs) have centered around a handful of
data-rich languages. What does it take to broaden access to breakthroughs beyond first …

Extending the context window of large language models (LLMs) is getting popular recently,
while the solution of augmenting LLMs with retrieval has existed for years. The natural …

Studying how people interact with large language models (LLMs) in real-world scenarios is
increasingly important due to their widespread use in various applications. In this paper, we …

Datasets are foundational to many breakthroughs in modern artificial intelligence. Many
recent achievements in the space of natural language processing (NLP) can be attributed to …

Theory of mind (ToM) evaluations currently focus on testing models using passive narratives
that inherently lack interactivity. We introduce FANToM, a new benchmark designed to stress …

In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-
augmented generation (RAG) and conversational question answering (QA). To enhance …

Human values are crucial to human decision-making.\textit {Value pluralism} is the view that
multiple correct values may be held in tension with one another (eg, when considering\textit …

Language models (LMs) with less than 100B parameters are known to perform poorly on
chain-of-thought (CoT) reasoning in contrast to large LMs when solving unseen tasks. In this …