Transformer plays a vital role in the realms of natural language processing (NLP) and
computer vision (CV), specially for constructing large language models (LLM) and large …

Scaling up language models to billions of parameters has opened up possibilities for in-
context learning, allowing instruction tuning and few-shot learning on tasks that the model …

Large language models (LLMs) have pushed the limits of natural language understanding
and exhibited excellent problem-solving ability. Despite the great success, most existing …

Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Reasoning is a fundamental aspect of human intelligence that plays a crucial role in
activities such as problem solving, decision making, and critical thinking. In recent years …

The surprising ability of Large Language Models (LLMs) to perform well on complex
reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very …

Recent works have shown that chain-of-thought (CoT) prompting can elicit language models
to solve complex reasoning tasks, step-by-step. However, prompt-based CoT methods are …

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 …

Chain of thought prompting successfully improves the reasoning capabilities of large
language models, achieving state of the art results on a range of datasets. However, these …

When writing and talking, people sometimes pause to think. Although reasoning-focused
works have often framed reasoning as a method of answering questions or completing …