Generated Knowledge

What is Generated Knowledge?

The idea behind the Generated Knowledge approach is to ask the Large Language Model (LLM) to generate potentially useful information about a given question/prompt before generating a final response.

For example, say you want to write a small blog post about some animal, like Spirit bears. Before asking the LLM to write the blog post, you can ask it to generate some facts about Spirit bears. This will help the LLM write a more informative blog post. There are two simple ways of going about this.

Single prompt generated knowledge approach

The first approach asks the LLM to generate knowledge and then write the post, all with a single prompt.

Generate 4 facts about the Kermode bear, then use these facts to write a short blog post using the information:

The Kermode bear, also known as the spirit bear, is a rare subspecies of the American black bear that is found in the coastal regions of British Columbia, Canada.

1. The Kermode bear is a unique subspecies of the American black bear, with approximately 10% of the population having white fur.

2. The Kermode bear is a symbol of the First Nations people of British Columbia and is seen as a spirit animal.

3. The Kermode bear is an important part of the ecosystem, helping to disperse seeds and maintain the balance of the forest.

4. The Kermode bear is a protected species, and its population is monitored closely by the provincial government.

The Kermode bear is a unique and special animal that is found in the coastal regions of British Columbia, Canada. Approximately 10% of the population of this subspecies of the American black bear have white fur, making them a rare sight. The Kermode bear is a symbol of the First Nations people of British Columbia and is seen as a spirit animal. It is an important part of the ecosystem, helping to disperse seeds and maintain the balance of the forest. The Kermode bear is a protected species, and its population is monitored closely by the provincial government. It is a beautiful and majestic animal, and we must do our part to protect and conserve this species.

Dual prompt generated knowledge approach

In this approach, we first prompt the LLM to generate facts about the bear:

Generate 4 facts about the Kermode bear:

1. The Kermode bear, also known as the Spirit Bear, is a rare subspecies of the American black bear found in British Columbia, Canada.

2. The Kermode bear has a unique white or cream-colored coat, which is caused by a recessive gene.

3. The Kermode bear is a symbol of hope and renewal for the First Nations people of British Columbia.

4. The Kermode bear is a protected species and is listed as a species of special concern by the Committee on the Status of Endangered Wildlife in Canada.

Then, we feed that information into another prompt to write the blog post:

1. The Kermode bear, also known as the Spirit Bear, is a rare subspecies of the American black bear found in British Columbia, Canada.

2. The Kermode bear has a unique white or cream-colored coat, which is caused by a recessive gene.

3. The Kermode bear is a symbol of hope and renewal for the First Nations people of British Columbia.

4. The Kermode bear is a protected species and is listed as a species of special concern by the Committee on the Status of Endangered Wildlife in Canada.

Use the above facts to write a one paragraph blog post about the Kermode bear:

The Kermode bear, also known as the Spirit Bear, is a rare subspecies of the American black bear found in British Columbia, Canada. This unique bear has a white or cream-colored coat, caused by a recessive gene, and is a symbol of hope and renewal for the First Nations people of British Columbia. The Kermode bear is a protected species and is listed as a species of special concern by the Committee on the Status of Endangered Wildlife in Canada, making it an important part of the region's biodiversity.

I recommend this second approach since it can produce longer content more reliably.

Another use case

The generated knowledge approach was actually introduced for a completely different task, that of answering difficult questions. Consider the following question, which GPT-3 answers incorrectly:

If we first ask LLM to generate facts about Congo and South Africa, we can then use that information to answer the question correctly. In theory, this is similar to Chain-of-Thought (CoT) Prompting, since we are effectively getting the LLM to generate intermediate reasoning in the form of related facts.

Let's start with the first step, knowledge generation. We can ask the LLM to generate facts about Congo and South Africa:

https://learnprompting.org/docs/intermediate/revisiting_roles

Next, let's use that knowledge to answer the question correctly. This is the knowledge integration step!

A more technical discussion

Although the above use case was similar to the way generated knowledge was originally introduced, it is not exactly the same. The below content covers the more technical context in which the approach was introduced. It follows the two intermediate steps (knowledge generation and knowledge integration) pattern that we saw above.

Knowledge Generation

In the knowledge generation step, the LLM is asked to generate a set of facts about the question. The LLM is prompted in Few-Shot fashion as seen below. M different completions are generated using this same prompt (similar to the self-consistency approach).

Knowledge Integration

Next, we generate "knowledge augmented" questions and prompt the LLM with them to get final answers. The easiest way to understand this is to go through an example.

Let's assume we are attempting to answer the question "Most Kangaroos have <mask> limbs". Assume that at the knowledge generation step we generated 2 knowledges (M=2):

• Knowledge 1: Kangaroos are marsupials that live in Australia.

• Knowledge 2: Kangaroos are marsupials that have 5 limbs.

Now, we concatenate each knowledge with the question to generate knowledge-augmented questions:

• Knowledge Augmented Question 1: Most Kangaroos have <mask> limbs. Kangaroos are marsupials that live in Australia.

• Knowledge Augmented Question 2: Most Kangaroos have <mask> limbs. Kangaroos are marsupials that have 5 limbs.

We then prompt the LLM with these knowledge-augmented questions and get the final answer proposals:

• Answer 1: 4

• Answer 2: 5

We select the answer with the highest probability as the final answer. The highest probability could be the softmax probability of the answer token, or the log probability of the answer token(s).

Recitation-Augmented Language Models

The recitation-augmented approach is similar to generated knowledge. However, is much less complex than the formal implementation of generated knowledge.

The idea here is to Few-Shot prompt the LLM to generate information and answer in the same step. The fact that it is reciting/generating knowledge and answering the question in the same step is the main difference from the generated knowledge approach.

To reiterate, this approach prompts the model with multiple (question, recitation, answer) exemplars, then asks the question. The authors note that this approach can be combined with self-consistency or multiple completion paths.

Notes

• Generated knowledge shows improvements on various commonsense datasets.

• The knowledge corresponding to the selected answer is called the selected knowledge.

• In practice, you could take the most frequently occurring answer as the final one.

Conclusion

Like Chain-of-Thought prompting, the generated knowledge approach prompts the model to first output information that will be relevant and useful to the final answer. Generating this information proves to be useful to the final task, as the answer becomes more informative, reliable, and accurate.

FAQ

How does the generated knowledge approach improve the accuracy of AI model responses?

Generating relevant information before outputting a final result helps the model anchor its response in factual information about the topic introduced by the input prompt, which can lead to more accurate and contextually grounded answers.

How is generated knowledge similar to Chain-of-Thought prompting?

Generated knowledge is a method of asking the LLM to generate potentially useful information before producing its final answer to the prompt, rather than generating a response to the input directly. This is similar to the Chain-of-Thought prompting technique as the LLM is effectively generating intermediate reasoning before outputting a final result, this time in the form of relevant facts.

What is the difference between the single prompt and dual prompt-generated knowledge approaches?

While the single prompt approach asks the model to generate knowledge and answer the question all at once, the dual prompt approach splits the two steps, thereby asking the model for relevant facts and background information and then asking the question in a follow-up input. This second approach is preferred since it can produce longer content more reliably.