Large language models (LLMs) have demonstrated an impressive ability to perform
arithmetic and symbolic reasoning tasks, when provided with a few examples at test time (" …

Abstract We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms such as …

Agents capable of carrying out general tasks on a computer can improve efficiency and
productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally …

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 …

Large language models have achieved impressive performance on various natural
language processing tasks. However, so far they have been evaluated primarily on …

Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to
solve various tasks. However, this approach struggles as the task complexity increases or …

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 …

Retrieval-augmented in-context learning has emerged as a powerful approach for
addressing knowledge-intensive tasks using frozen language models (LM) and retrieval …

Recent efforts have incorporated large language models (LLMs) with external resources (eg,
the Internet) or internal control flows (eg, prompt chaining) for tasks requiring grounding or …

Large language models have demonstrated outstanding performance on a wide range of
tasks such as question answering and code generation. On a high level, given an input, a …