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@ -4,12 +4,16 @@ In this section, we discuss other miscellaneous but important topics in prompt e
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**Note that this section is under construction.**
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## Program-Aided Language Models
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[Gao et al., (2023)](https://arxiv.org/abs/2211.10435) presents a method that uses LLMs to read natural language problems and generate programs as the intermediate reasoning steps. Coined, program-aided language models (PAL), it differs from chain-of-thought prompting in that instead of using free-form text to obtain solution it offloads the solution step to a programmatic runtime such as a Python interpreter.
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Full example coming soon!
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---
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## Multimodal Prompting
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In this section, we will cover some examples of multimodal prompting techniques and applications that leverage multiple modalities as opposed to just text alone.
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More coming soon!
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Examples coming soon!
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---
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## GraphPrompts
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@ -46,7 +46,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@ -55,7 +55,7 @@
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"True"
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]
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},
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"execution_count": 1,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -64,6 +64,7 @@
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"import openai\n",
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"import os\n",
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"import IPython\n",
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"from langchain.llms import OpenAI\n",
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"from dotenv import load_dotenv\n",
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"load_dotenv()"
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]
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@ -541,7 +542,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "minprompts",
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"display_name": "promptlecture",
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"language": "python",
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"name": "python3"
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},
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@ -555,12 +556,12 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.15"
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"version": "3.9.16"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "872fbaa170678d9803e866eb8aab13838cd416716b835df572a04d4d73e81a04"
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"hash": "f38e0373277d6f71ee44ee8fea5f1d408ad6999fda15d538a69a99a1665a839d"
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}
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}
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},
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