└── README.md


/README.md:
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 1 | # COT-Reading-List
 2 | 
 3 | ## 基础论文
 4 | - Chain-of-Thought Prompting Elicits Reasoning in Large Language Models https://arxiv.org/abs/2201.11903
 5 | - Large Language Models are Zero-Shot Reasoners https://arxiv.org/abs/2205.11916
 6 | - Automatic Chain of Thought Prompting in Large Language Models https://arxiv.org/abs/2210.03493
 7 | 
 8 | ## 问题分解
 9 | - Least-to-Most Prompting Enables Complex Reasoning in Large Language Models https://arxiv.org/abs/2205.10625
10 | - Measuring and Narrowing the Compositionality Gap in Language Models https://arxiv.org/abs/2210.03350
11 | 
12 | ## 融合预测
13 | - Self-Consistency Improves Chain of Thought Reasoning in Language Models https://arxiv.org/abs/2203.11171
14 | - Active Prompting with Chain-of-Thought for Large Language Models https://arxiv.org/abs/2302.12246
15 | - Rationale-Augmented Ensembles in Language Models https://arxiv.org/abs/2207.00747
16 | 
17 | ## 生成-校验
18 | - STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning https://arxiv.org/abs/2203.14465
19 | - On the Advance of Making Language Models Better Reasoners https://arxiv.org/abs/2206.02336
20 | 
21 | ## 多语言
22 | - Language Models are Multilingual Chain-of-Thought Reasoners https://arxiv.org/abs/2210.03057
23 | 
24 | ## 大模型背景
25 | - PaLM: Scaling Language Modeling with Pathways https://arxiv.org/abs/2204.02311
26 | - Emergent Abilities of Large Language Models https://arxiv.org/abs/2206.07682
27 | - Language Model Cascades https://arxiv.org/abs/2207.10342
28 | 
29 | 2022.10.24
30 | 


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