Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang et al., 2023, Self-Consistency Improves Chain of Thought Reasoning in Language Models (The Eleventh International Conference on Learning Representations (ICLR 2023))
Subscriber deck — free during early access
1 cards in “3.1 Experiment Setup” · whole deck
3.1 Experiment Setup
- How were self-consistency’s experiments set up?Arithmetic, commonsense and symbolic reasoning benchmarks; four models (UL2-20B, GPT-3 175B via Codex engines, LaMDA-137B, PaLM-540B); few-shot prompting with the same chain-of-thought exemplars as Wei et al. (2022) and no training; 40 sampled paths per question, averaged over 10 runs.
- Wang et al., 2023, Self-Consistency Improves Chain of Thought Reasoning in Language Models (The Eleventh International Conference on Learning Representations (ICLR 2023)), §3.1
Source
- Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery and Denny Zhou. 2023. Self-Consistency Improves Chain of Thought Reasoning in Language Models. In The Eleventh International Conference on Learning Representations (ICLR 2023). https://arxiv.org/abs/2203.11171