Who cites «STaR: Bootstrapping Reasoning With Reasoning» · page 5
- FOLIO: Natural Language Reasoning with First-Order Logic — 2022 · arXiv:2209.00840
- Faithful Reasoning Using Large Language Models — 2022 · arXiv:2208.14271
- Few-Shot Self-Rationalization with Natural Language Prompts — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
- Inferring Implicit Relations in Complex Questions with Language Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
- Instruction Induction: From Few Examples to Natural Language Task Descriptions — 2022 · arXiv:2205.10782
- Language Model Cascades — 2022 · arXiv:2207.10342
- Language models show human-like content effects on reasoning tasks — 2022 · arXiv:2207.07051
- Large Language Models Are Human-Level Prompt Engineers — 2022 · arXiv:2211.01910
- Large language models are zero-shot reasoners — 2022
- Learning by Distilling Context — 2022 · arXiv:2209.15189
- Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
- Legal Prompting: Teaching a Language Model to Think Like a Lawyer — 2022 · arXiv:2212.01326
- ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning — 2022 · arXiv:2212.07919
- Rationale-Augmented Ensembles in Language Models — 2022 · arXiv:2207.00747
- ReAct: Synergizing Reasoning and Acting in Language Models — 2022 · arXiv:2210.03629
- Reasoning with Language Model Prompting: A Survey — 2022 · arXiv:2212.09597
- Scaling Instruction-Finetuned Language Models — 2022 · arXiv:2210.11416
- Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning — 2022 · arXiv:2205.09712
- Solving math word problems with process- and outcome-based feedback — 2022 · arXiv:2211.14275
- Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks — 2022 · arXiv:2204.02892
- Successive Prompting for Decomposing Complex Questions — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Teaching Algorithmic Reasoning via In-context Learning — 2022 · arXiv:2211.09066
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