Prompting large language models (LLMs) is a challenging task that requires careful consideration of the prompt’s structure and content. In this blog article, I will explore three different techniques for prompting: Chain-of-Verification, Chain-of-Density, and Chain-of-Thought. I will provide examples of how each technique works and how it can be used to generate high-quality responses.
Chain of Verification
Research Paper: 2309.11495.pdf (arxiv.org)
The Chain-of-Verification (CoVe) method is a technique to improve accuracy in large language models. It works by having the model first draft an initial response, and then plan verification questions to fact-check its draft. The model then answers those questions independently so the answers are not biased by other responses. Finally, it generates its final verified response
The CoVe method is designed to address the issue of hallucination in large language models. Hallucination refers to the generation of plausible yet incorrect factual information. This is a significant issue because it can lead to misinformation and errors in various applications such as question answering and summarization
The CoVe method is based on the idea of deliberation. Deliberation involves having the model think through its responses and consider alternative possibilities before generating a final response. In the case of CoVe, this involves having the model generate an initial response, then fact-checking that response with verification questions. By doing so, the model can identify and correct any errors or inaccuracies in its initial response before generating a final verified response.
Here’s an example of how CoVe works:
Simple Prompt
Chain of Verification Prompt
Chain of Density
Research Paper: 2309.04269.pdf (arxiv.org)
The technique called Chain of Density (CoD) prompting has been introduced to improve the quality of summarizations. This technique aims to generate increasingly concise and entity-dense summaries.
The process starts with an initial entity-sparse summary. The model iteratively incorporates missing salient entities without increasing the length of the summary. This results in summaries that are more abstractive, exhibit more fusion and have less lead bias than those generated by a vanilla prompt.
Here’s an example of how CoD works:
Simple Prompt
Chain of Density Prompt
Chain-of-Thought
Research Paper: 2201.11903.pdf (arxiv.org)
The Chain-of-Thought (CoT) method is a technique that breaks down complex queries or tasks into a series of interconnected prompts. Instead of relying on a single input, the model is guided through a sequence of prompts that refine and build upon each other.
Here’s an example of how CoT works:
Simple Prompt
Chain of Thought Prompt
Summary
In this blog article, the intricacies of prompting large language models (LLMs) are explored, focusing on three innovative techniques: Chain-of-Verification (CoVe), Chain-of-Density (CoD), and Chain-of-Thought (CoT). The CoVe method, devised to combat the hallucination issue in LLMs, uses a deliberative approach. It prompts the model to draft a response, fact-check it through verification questions, and finalize a corrected version. The CoD technique aims at enhancing summary quality, commencing with an entity-sparse summary and iteratively integrating crucial entities to create concise and information-dense summaries. On the other hand, the CoT method manages complex queries by fragmenting them into a series of evolving prompts. Each technique offers a unique way to refine LLM responses, ensuring accuracy, richness, and relevance.
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