commit
c5d506c75d
|
@ -14,7 +14,7 @@ The first step involves a large language model (as an inference model) that is g
|
||||||
|
|
||||||
APE discovers a better zero-shot CoT prompt than the human engineered "Let's think step by step" prompt ([Kojima et al., 2022](https://arxiv.org/abs/2205.11916)).
|
APE discovers a better zero-shot CoT prompt than the human engineered "Let's think step by step" prompt ([Kojima et al., 2022](https://arxiv.org/abs/2205.11916)).
|
||||||
|
|
||||||
The prompt "Let's work this out it a step by step to be sure we have the right answer." elicits chain-of-though reasoning and improves performance on the MultiArith and GSM8K benchmarks:
|
The prompt "Let's work this out in a step by step way to be sure we have the right answer." elicits chain-of-though reasoning and improves performance on the MultiArith and GSM8K benchmarks:
|
||||||
|
|
||||||
<Screenshot src={APECOT} alt="APECOT" />
|
<Screenshot src={APECOT} alt="APECOT" />
|
||||||
Image Source: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910)
|
Image Source: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910)
|
||||||
|
@ -23,4 +23,4 @@ This paper touches on an important topic related to prompt engineering which is
|
||||||
|
|
||||||
- [AutoPrompt](https://arxiv.org/abs/2010.15980) - proposes an approach to automatically create prompts for a diverse set of tasks based on gradient-guided search.
|
- [AutoPrompt](https://arxiv.org/abs/2010.15980) - proposes an approach to automatically create prompts for a diverse set of tasks based on gradient-guided search.
|
||||||
- [Prefix Tuning](https://arxiv.org/abs/2101.00190) - a lightweight alternative to fine-tuning that prepends a trainable continuous prefix for NLG tasks.
|
- [Prefix Tuning](https://arxiv.org/abs/2101.00190) - a lightweight alternative to fine-tuning that prepends a trainable continuous prefix for NLG tasks.
|
||||||
- [Prompt Tuning](https://arxiv.org/abs/2104.08691) - proposes a mechanism for learning soft prompts through backpropagation.
|
- [Prompt Tuning](https://arxiv.org/abs/2104.08691) - proposes a mechanism for learning soft prompts through backpropagation.
|
||||||
|
|
Loading…
Reference in New Issue