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Papers on “large language models reasoning chain of thought”

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1. LLaMA: Open and Efficient Foundation Language Models

   Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al. · 2023 · arXiv (Cornell University) · 3,966 cites

   We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.

2. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

   Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al. · 2023 · PLOS Digital Health · 3,786 cites

   We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.

3. Large language models encode clinical knowledge

   Karan Singhal, Shekoofeh Azizi, Tao Tu, et al. · 2023 · Nature · 3,721 cites

   Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Lan

4. PaLM: Scaling Language Modeling with Pathways

   Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al. · 2022 · arXiv (Cornell University) · 2,136 cites

   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 number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning resu

5. Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models

   Jason Wei, Xuezhi Wang, Dale Schuurmans, et al. · 2022 · 1,792 cites

6. A Survey of Large Language Models

   Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. · 2026 · Frontiers of Computer Science · 1,517 cites

   Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including superv

7. Large Language Models are Zero-Shot Reasoners

   Takeshi Kojima, Shixiang Gu, Machel Reid, et al. · 2022 · arXiv (Cornell University) · 1,117 cites

   Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding "Let's think step by step" before each answer. Experi

8. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

   Daya Guo, Dejian Yang, Haowei Zhang, et al. · 2025 · Nature · 912 cites

   Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs) 1,2 and chain-of-thought (CoT) prompting 3 , have achieved considerable success on foundational reasoning tasks. However, this success is heavily contingent on extensive human-annotated demonstrations and the capabilities of models are still insufficient for more complex problems. Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labelled reasoning trajectories. The proposed RL framework facilitates the emergent development of