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@@ -5,11 +5,25 @@ In this section, we discuss other miscellaneous but important topics in prompt e
 **Note that this section is under construction.**
 
 Topic:
+- [Directional Stimulus Prompting](#directional-stimulus-prompting)
 - [Program-Aided Language Models](#program-aided-language-models)
 - [ReAct](#react)
 - [Multimodal CoT Prompting](#multimodal-prompting)
 - [GraphPrompts](#graphprompts)
 
+---
+
+## Directional Stimulus Prompting
+[Li et al., (2023)](https://arxiv.org/abs/2302.11520) proposes a new prompting technique to better guide the LLM in generating the desired summary.
+
+A tuneable policy LM is trained to generate the stimulus/hint. Seeing more use of RL to optimize LLMs.
+
+The figure below shows how Directional Stimulus Prompting compares with standard prompting. The policy LM can be small and optimized to generate the hints that guide a black-box frozen LLM.
+
+![](../img/dsp.jpeg)
+
+Full example coming soon!
+
 ---
 ## Program-Aided Language Models
 [Gao et al., (2022)](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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