This survey paper proposes a clearer view of natural language reasoning in the field of
Natural Language Processing (NLP), both conceptually and practically. Conceptually, we …

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 …

We investigate the ability of language models to perform compositional reasoning tasks
where the overall solution depends on correctly composing the answers to sub-problems …

Abstract Large Language Models (LLMs) have demonstrated impressive performance in
various NLP tasks, but they still suffer from challenges such as hallucination and weak …

Code-generating large language models map natural language to code. However, only a
small portion of the infinite space of naturalistic utterances is effective at guiding code …

We present Step-Back Prompting, a simple prompting technique that enables LLMs to do
abstractions to derive high-level concepts and first principles from instances containing …

We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-
intrinsic reasoning structures to tackle complex reasoning problems that are challenging for …

Visual question answering (VQA) has traditionally been treated as a single-step task where
each question receives the same amount of effort, unlike natural human question-answering …

Large Language Models (LLMs) have shown remarkable results on various complex
reasoning benchmarks. The reasoning capabilities of LLMs enable them to execute function …

Instruction tuning of language models has demonstrated the ability to enhance model
generalization to unseen tasks via in-context learning using a few examples. However …