Active-Prompt

Chain-of-thought (CoT) methods rely on a fixed set of human-annotated exemplars. The problem with this is that the exemplars might not be the most effective examples for the different tasks. To address this, Diao et al., (2023) recently proposed a new prompting approach called Active-Prompt to adapt LLMs to different task-specific example prompts (annotated with human-designed CoT reasoning).

Below is an illustration of the approach. The first step is to query the LLM with or without a few CoT examples. k possible answers are generated for a set of training questions. An uncertainty metric is calculated based on the k answers (disagreement used). The most uncertain questions are selected for annotation by humans. The new annotated exemplars are then used to infer each question.

Image Source: Diao et al., (2023)

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.

Image Source: Li et al., (2023)