Abstract Generative Artificial Intelligence (AI) is one of the most exciting developments in
Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has …

Like humans, large language models (LLMs) do not always generate the best output on their
first try. Motivated by how humans refine their written text, we introduce Self-Refine, an …

This survey reviews works in which language models (LMs) are augmented with reasoning
skills and the ability to use tools. The former is defined as decomposing a potentially …

While large language models (LMs) have shown remarkable capabilities across numerous
tasks, they often struggle with simple reasoning and planning in physical environments …

Reasoning, as an essential ability for complex problem-solving, can provide back-end
support for various real-world applications, such as medical diagnosis, negotiation, etc. This …

Despite their unprecedented success, even the largest language models make mistakes.
Similar to how humans learn and improve using feedback, previous work proposed …

Despite their competitive performance on knowledge-intensive tasks, large language
models (LLMs) still have limitations in memorizing all world knowledge especially long tail …

In this paper, we propose a novel task IntentQA, a special VideoQA task focusing on video
intent reasoning, which has become increasingly important for AI with its advantages in …

Table-based reasoning has shown remarkable progress in a wide range of table-based
tasks. It is a challenging task, which requires reasoning over both free-form natural language …

This paper reviews the state-of-the-art of language models architectures and strategies for"
complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large …