Natural language reasoning, a survey

F Yu, H Zhang, P Tiwari, B Wang - ACM Computing Surveys, 2024 - dl.acm.org
This survey article proposes a clearer view of Natural Language Reasoning (NLR) in the
field of Natural Language Processing (NLP), both conceptually and practically …

A survey of large language models

WX Zhao, K Zhou, J Li, T Tang, X Wang, Y Hou… - arXiv preprint arXiv …, 2023 - arxiv.org
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Pal: Program-aided language models

L Gao, A Madaan, S Zhou, U Alon… - International …, 2023 - proceedings.mlr.press
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 (" …

Towards reasoning in large language models: A survey

J Huang, KCC Chang - arXiv preprint arXiv:2212.10403, 2022 - arxiv.org
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 …

Making language models better reasoners with step-aware verifier

Y Li, Z Lin, S Zhang, Q Fu, B Chen… - Proceedings of the …, 2023 - aclanthology.org
Few-shot learning is a challenging task that requires language models to generalize from
limited examples. Large language models like GPT-3 and PaLM have made impressive …

Palm: Scaling language modeling with pathways

A Chowdhery, S Narang, J Devlin, M Bosma… - Journal of Machine …, 2023 - jmlr.org
Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

Chain-of-thought prompting elicits reasoning in large language models

J Wei, X Wang, D Schuurmans… - Advances in neural …, 2022 - proceedings.neurips.cc
We explore how generating a chain of thought---a series of intermediate reasoning steps---
significantly improves the ability of large language models to perform complex reasoning. In …

Active prompting with chain-of-thought for large language models

S Diao, P Wang, Y Lin, R Pan, X Liu… - arXiv preprint arXiv …, 2023 - arxiv.org
The increasing scale of large language models (LLMs) brings emergent abilities to various
complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is …

Synthetic prompting: Generating chain-of-thought demonstrations for large language models

Z Shao, Y Gong, Y Shen, M Huang… - International …, 2023 - proceedings.mlr.press
Large language models can perform various reasoning tasks by using chain-of-thought
prompting, which guides them to find answers through step-by-step demonstrations …

Automatic prompt augmentation and selection with chain-of-thought from labeled data

KS Shum, S Diao, T Zhang - arXiv preprint arXiv:2302.12822, 2023 - arxiv.org
Chain-of-thought prompting (CoT) advances the reasoning abilities of large language
models (LLMs) and achieves superior performance in arithmetic, commonsense, and …