The Prompt Report: A Systematic Survey of Prompting Techniques

I have the v2, published Dec 30

We note that the transition dynamics between states depend primarily on the verb used in the action (e.g., take, put, cook, ...).

Predicting action-driven transitions is easier than predicting environment-driven transitions: At best, GPT-4 is able to simulate 77.1% of dynamic action-driven transitions correctly. In contrast, GPT-4 simulates at most 49.7% of dynamic environment-driven transitions correctly. This indicates that the most challenging part of the LLMSim task is likely simulating the underlying environmental dynamics.

there are many poorly understood definitions (e.g. prompt, prompt engineering) and conflicting ones (e.g. role prompt vs persona prompt). The lack of a consistent vocabulary hampers the community’s ability to clearly describe the various prompting techniques in use.

Prompt Chain A prompt chain (activity: prompt chaining) consists of two or more prompt templates used in succession. The output of the prompt generated by the first prompt template is used to parameterize the second template, continuing until all templates are exhausted

Prompting Technique A prompting technique is a blueprint that describes how to structure a prompt, prompts, or dynamic sequencing of multiple prompts. A prompting technique may incorporate conditional or branching logic, parallelism, or other architectural considerations spanning multiple prompts.

2.2.6 Self-Criticism

When creating GenAI systems, it can be useful to have LLMs criticize their own outputs (Huang et al., 2022). This could simply be a judgement (e.g., is this output correct) or the LLM could be prompted to provide feedback, which is then used to improve the answer. Many approaches to generating and integrating self-criticism have been developed. Self-Calibration (Kadavath et al., 2022) first prompts an LLM to answer a question. Then, it builds a new prompt that includes the question, the LLM’s answer, and an additional instruction asking whether the answer is correct. This can be useful for gauging confidence levels when applying LLMs when deciding when to accept or revise the original answer.

Self-Refine (Madaan et al., 2023) is an iterative framework where, given an initial answer from the LLM, it prompts the same LLM to provide feedback on the answer, and then prompts the LLM to improve the answer based on the feedback. This iterative process continues until a stopping condition is met (e.g., max number of steps reached). Self-Refine has demonstrated improvement across a range of reasoning, coding, and generation tasks.

Reversing Chain-of-Thought (RCoT) (Xue et al., 2023) first prompts LLMs to reconstruct the problem based on generated answer. Then, it generates fine-grained comparisons between the original problem and the reconstructed problem as a way to check for any inconsistencies. These inconsistencies are then converted to feedback for the LLM to revise the generated answer. Self-Verification (Weng et al., 2022) generates multiple candidate solutions with Chain-of- Thought (CoT). It then scores each solution by masking certain parts of the original question and asking an LLM to predict them based on the rest of the question and the generated solution. This method has shown improvement on eight reasoning datasets.

Chain-of-Verification (COVE) (Dhuliawala et al., 2023) first uses an LLM to generate an answer to a given question. Then creates a list of related questions that would help verify the correctness of the answer. Each question is answered by the LLM, then all the information is given to the LLM to produce the final revised answer. This method has shown improvements in various question-answering and text-generation tasks.

Cumulative Reasoning (Zhang et al., 2023b) first generates several potential steps in answering the question. It then has a LLM evaluate them, deciding to either accept or reject these steps. Finally, it checks whether it has arrived at the final answer. If so, it terminates the process, but otherwise it repeats it. This method has demonstrated improvements in logical inference tasks and mathematical problem.