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 …

The widespread adoption of large language models (LLMs) makes it important to recognize
their strengths and limitations. We argue that in order to develop a holistic understanding of …

As transformers have gained prominence in natural language processing, some researchers
have investigated theoretically what problems they can and cannot solve, by treating …

Automated reasoning with unstructured natural text is a key requirement for many potential
applications of NLP and for developing robust AI systems. Recently, Language Models …

Are LLMs cultural technologies like photocopiers or printing presses, which transmit
information but cannot create new content? A challenge for this idea, which we call …

This paper provides a comprehensive overview of the evolution and latest advancements in
Generative AI models, alongside their economic impact and applications in asset …

State-space models (SSMs) have emerged as a potential alternative architecture for building
large language models (LLMs) compared to the previously ubiquitous transformer …

Fine-tuning on generalized tasks such as instruction following, code generation, and
mathematics has been shown to enhance language models' performance on a range of …

In-context learning (ICL) is now a common method for supervising large language models
(LLMs): given labeled examples in the input context, the LLM learns to perform the task …

Language models are susceptible to bias, sycophancy, backdoors, and other tendencies
that lead to unfaithful responses to the input context. Interpreting internal states of language …