Rigorous and reproducible evaluation is critical for assessing the state of the art and for
guiding scientific advances in Artificial Intelligence. Evaluation is challenging in practice due …

LLMs have shown remarkable capabilities, but precisely controlling their response behavior
remains challenging. Existing activation steering methods alter LLM behavior …

In the rapidly evolving field of Large Language Models (LLMs), ensuring safety is a crucial
and widely discussed topic. However, existing works often overlook the geo-diversity of …

Existing evaluation frameworks for retrieval-augmented generation (RAG) systems focus on
answerable queries, but they overlook the importance of appropriately rejecting …

The ideal LLM content moderation system would be both structurally interpretable (so its
decisions can be explained to users) and steerable (to reflect a community's values or align …

Customer Relationship Management (CRM) systems are vital for modern enterprises,
providing a foundation for managing customer interactions and data. Integrating AI agents …

Language models are now deployed in a wide variety of user-facing applications, often for
specific purposes like answering questions about documentation or acting as coding …

Role-Playing Agents (RPAs) have shown remarkable performance in various applications,
yet they often struggle to recognize and appropriately respond to hard queries that conflict …

Despite their impressive ability to generate high-quality and fluent text, generative large
language models (LLMs) also produce hallucinations: statements that are misaligned with …

Large language models (LLMs) often exhibit undesirable behaviors, such as hallucinations
and sequence repetitions. We propose to view these behaviors as fallbacks that models …