Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various
tasks but remain as black-box systems. Consequently, the reasoning bottlenecks of LLMs …

It is crucial for large language models (LLMs) to follow instructions that involve multiple
constraints. However, soft constraints are semantically related and difficult to verify through …

While recent efforts have begun integrating large language models (LLMs) into foreign
language education (FLE), they often rely on traditional approaches to learning tasks without …

Safety alignment is an essential research topic for real-world AI applications. Despite the
multifaceted nature of safety and trustworthiness in AI, current safety alignment methods …

Large language models (LLMs) struggle with precise length control, particularly in zero-shot
settings. We conduct a comprehensive study evaluating LLMs' length control capabilities …

Abstract Large Language Models (LLMs) gained notable popularity in academia and
industry. It has unprecedented features and performance in many applications. LLMs are …

We present a large-scale evaluation of 30 cognitive biases in 20 state-of-the-art large
language models (LLMs) under various decision-making scenarios. Our contributions …

Ensuring Artificial General Intelligence (AGI) reliably avoids harmful behaviors is a critical
challenge, especially for systems with high autonomy or in safety-critical domains. Despite …

Story understanding and generation have long been a challenging task in natural language
processing (NLP), especially when dealing with various levels of instruction specificity. In …

Large language models (LLMs) have emerged as powerful tools for (semi-) automating the
initial screening of abstracts in systematic reviews, offering the potential to significantly …