Online education platforms, leveraging the internet to distribute education resources, seek to
provide convenient education but often fall short in real-time communication with students …

Pretrained language models often generate outputs that are not in line with human
preferences, such as harmful text or factually incorrect summaries. Recent work approaches …

As large language models improve, there is increasing interest in techniques that leverage
these models' capabilities to refine their own outputs. In this work, we introduce Shepherd, a …

Text summarization systems have made significant progress in recent years, but typically
generate summaries in one single step. However, the one-shot summarization setting is …

Evaluating the factual consistency of automatically generated summaries is essential for the
progress and adoption of reliable summarization systems. Despite recent advances, existing …

As humans, we consistently engage in interactions with our peers and receive feedback in
the form of natural language. This language feedback allows us to reflect on our actions …

Recent work has explored the capability of large language models (LLMs) to identify and
correct errors in LLM-generated responses. These refinement approaches frequently …

Language models as a service (LMaaS) enable users to accomplish tasks without requiring
specialized knowledge, simply by paying a service provider. However, numerous providers …

Revising scientific papers based on peer feedback is a challenging task that requires not
only deep scientific knowledge and reasoning, but also the ability to recognize the implicit …

LLMs can generate factually incorrect statements even when provided access to reference
documents. Such errors can be dangerous in high-stakes applications (eg, document …