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Miscellaneous Topics
In this section, we discuss other miscellaneous but important topics in prompt engineering.
Note that this section is under construction.
Topic: - Directional Stimulus Prompting - Program-Aided Language Models - ReAct - Multimodal CoT Prompting - GraphPrompts
Directional Stimulus Prompting
Li et al., (2023) 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.
Full example coming soon!
ReAct
Yao et al., 2022 introduced a framework where LLMs are used to generate both reasoning traces and task-specific actions in an interleaved manner. Generating reasoning traces allow the model to induce, track, and update action plans, and even handle exceptions. The action step allows to interface with and gather information from external sources such as knowledge bases or environments.
The ReAct framework can allow LLMs to interact with external tools to retrieve additional information that leads to more reliable and factual responses.
Full example coming soon!
GraphPrompts
Liu et al., 2023 introduces GraphPrompt, a new prompting framework for graphs to improve performance on downstream tasks.
More coming soon!