With generative artificial intelligence (GenAI), particularly large language models (LLMs),
continuing to make inroads in healthcare, assessing LLMs with human evaluations is …

Evaluating the factuality of long-form text generated by large language models (LMs) is non-
trivial because (1) generations often contain a mixture of supported and unsupported pieces …

Abstract Language models (LMs) often exhibit undesirable text generation behaviors,
including generating false, toxic, or irrelevant outputs. Reinforcement learning from human …

Evaluation of Large Language Models (LLMs) is challenging because aligning to human
values requires the composition of multiple skills and the required set of skills varies …

As large language models (LLMs) advance, it becomes more challenging to reliably
evaluate their output due to the high costs of human evaluation. To make progress towards …

By simply composing prompts, developers can prototype novel generative applications with
Large Language Models (LLMs). To refine prototypes into products, however, developers …

Summarizing book-length documents (> 100K tokens) that exceed the context window size
of large language models (LLMs) requires first breaking the input document into smaller …

As language models are adapted by a more sophisticated and diverse set of users, the
importance of guaranteeing that they provide factually correct information supported by …

LLMs and RAG systems are now capable of handling millions of input tokens or more.
However, evaluating the output quality of such systems on long-context tasks remains …

Evaluating retrieval-augmented generation (RAG) systems traditionally relies on hand
annotations for input queries, passages to retrieve, and responses to generate. We …