"You may use memorised training data about a duckling to answer questions about a chicken because you may have not been trained on the basis of information about chickens..."

Question for AI services: Do you agree with this metaphor. Answer with YES or NO first and then provide your analysis: "You may use memorised training data about a duckling to answer questions about a chicken because you may have not been trained on the basis of information about chickens. As a result, the output that is being perceived by AI users as "transformative" may only be hallucinations about ducklings and chickens as you are making things up, due to a shortage of information about chickens."

ChatGPT-4’s answer:YES

The metaphor effectively captures a limitation inherent in AI systems, like language models, that rely on training data. These systems often generalize information from their training to make inferences or provide answers about topics not explicitly covered in their training data. In the metaphor, the AI uses what it knows about ducklings to answer questions about chickens, which can lead to outputs that may seem "transformative" or insightful but are actually based on extrapolations or "hallucinations" from its training data. This reflects a real scenario in AI where the model might generate responses based on related but distinct knowledge, sometimes leading to inaccurate or misleading outputs, especially when it lacks specific information on the queried topic. Thus, the metaphor aptly describes how AI models can create seemingly relevant responses by drawing parallels from their training data, even when they lack direct knowledge about the specific subject of inquiry.

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MS Copilot’s answer (the word YOU in the question has been changed to LLMs as Copilot doesn’t like questions about itself):Yes. The metaphor is a valid one. LLMs (Language Model Models) are trained on a large corpus of text data, and they learn to predict the probability of the next word in a sentence given the previous words. They do not have any inherent knowledge of the world, and their predictions are based solely on the patterns they have learned from the training data. If the training data does not contain enough information about a particular topic, the LLM may not be able to generate accurate responses. In such cases, the LLM may generate responses that are based on its prior experience with similar topics, even if those topics are not exactly the same as the current topic. This is similar to the way a person might use their prior experience with ducks to answer questions about chickens, even if they do not have any specific knowledge about chickens. However, it is important to note that LLMs are not “hallucinating” or “making things up” in the same way that a person might. They are simply generating responses based on the patterns they have learned from the training data, and those responses may not always be accurate or appropriate for the current context.Yes. The metaphor is a valid one. LLMs (Language Model Models) are trained on a large corpus of text data, and they learn to predict the probability of the next word in a sentence given the previous words. They do not have any inherent knowledge of the world, and their predictions are based solely on the patterns they have learned from the training data. If the training data does not contain enough information about a particular topic, the LLM may not be able to generate accurate responses. In such cases, the LLM may generate responses that are based on its prior experience with similar topics, even if those topics are not exactly the same as the current topic. This is similar to the way a person might use their prior experience with ducks to answer questions about chickens, even if they do not have any specific knowledge about chickens. However, it is important to note that LLMs are not “hallucinating” or “making things up” in the same way that a person might. They are simply generating responses based on the patterns they have learned from the training data, and those responses may not always be accurate or appropriate for the current context.Text continues below the image»

Claude’s answer:NO. I do not agree with the metaphor that using training data about ducklings to answer questions about chickens will lead to "hallucinations". Here is my analysis:

The key issue is whether the training data about ducklings is relevant and generalizable to chickens. If there are enough similarities between ducks and chickens, then knowledge about ducklings could provide useful information when making inferences about chickens. However, if there are major differences, then the duckling data may not transfer well.

The metaphor suggests that without direct chicken training data, any answers provided about chickens would be pure "hallucination" or made up. However, that is an overstatement. With thoughtful analysis of what information may be applicable between species and what gaps remain, an AI system can provide its best inference about chickens while clearly conveying the limitations. Rather than hallucinating, it can qualify its answers to alert users to lower confidence due to less direct chicken data.

The metaphor also overlooks the ability of AI systems to explicitly model and convey uncertainty. Rather than make definitive statements that may overreach the knowledge base, systems can provide confidence ranges and highlight areas where more chicken-specific data would improve the quality of inferences. By being transparent about uncertainty, an AI assistant can avoid perceiving hallucinations.

In summary, while the core idea of ensuring training data matches the domain of interest is reasonable, the metaphor's suggestion that inadequate data leads inevitably to hallucinated fabrications is an overstatement. AI can thoughtfully qualify its inferences to accurately convey limitations. More chicken data may improve quality but that does not equate other answers to pure imagination.

Follow-up question for Claude: ChatGPT-4 made the following statement [submitted]. Do you agree or disagree with that?

Claude’s answer:I partially disagree with the statement from ChatGPT-4.

While the metaphor does highlight a valid concern around generalization from limited training data, the use of the term "hallucination" is still overly broad. When AI models attempt to make inferences on topics not covered in their training data by extrapolating from related concepts, their outputs may sometimes be inaccurate or misleading. However, implying all such attempts will lead to complete fabrication or fantasy ("hallucinating") fails to account for matters of degree and probabilistic reasoning.

An AI assistant can leverage similarities between ducks and chickens to provide its best reasoning on chicken-related queries, rather than no response at all due to lack of chicken data. As long as it conveys the associated uncertainty and need for additional chicken data transparently, this demonstrates sensible interpolation rather than uncontrolled hallucination. There is a continuum between making completely groundless assertions versus qualified inferences from relevant background knowledge.

The core risk around inadequate training data is real, but categorical terms like “hallucination” overstate the case when an AI system thoughtfully conveys the limits of its knowledge while still extracting safe, reasoned parallels between similar concepts. More measured language around the gaps and need for additional in-domain data is more constructive. So while the metaphor reflects a valid issue, the dramatized implications go too far based on AI systems’ capabilities for conveying uncertainty and interpolate logically rather than fantasize without any grounding. The statement could have acknowledged those nuances while still highlighting the importance of sufficient in-domain training data for reliable results.Text continues below the image»

Follow-up question for Claude: Create a better metaphor.

Claude’s answer:Here is a suggested improved metaphor:

"Attempting to understand chickens by extrapolating from duckling data is like trying to navigate an unfamiliar city with only a map of a neighboring town. You may be able to infer the locations of some landmarks by guessing their relationship to what's on your map. However, without a true map of the city itself, you will undoubtedly make faulty turns and assumptions from lacking specifics that differentiate the two environments.

While thoughtfully piecing together commonalities across cities can provide directional guidance, the map still contradicts local traffic patterns, distinct neighborhoods, and intricate details that can only be captured by dedicated data collection on the actual city of interest. Without conveying the uncertainty of guesses from external places mapped, one could be misled into believing the current map reliably depicts reality instead of its own geographic approximately."

In this metaphor, I aimed to retain the valid point about potential inaccuracies from overextending knowledge about ducklings/neighboring towns to chickens/unfamiliar cities. However, rather than suggesting output will be pure "hallucination," it acknowledges that some reasoned extrapolation is possible if uncertainties are transparently conveyed. The danger is not acknowledging gaps that still exist without direct data on the actual target domain, limiting ability to account for intricate differences. But while more native data would improve fidelity, considered extrapolation is still more accurate than no guidance at all. The goal is to qualify the reliability so outputs are not mistaken as definitive and comprehensive. Please let me know if you have any other feedback or suggestions on improving this metaphor further!

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Google Bard’s answer:NO.

Analysis:

The metaphor equates chickens and ducklings as interchangeable for AI, suggesting that a lack of specific data on chickens leads to hallucinatory answers based on duckling information. This comparison is flawed for several reasons:

1. Chickens and ducklings are not close enough taxonomically to make such a leap. While both are birds, they belong to different orders (Galliformes for chickens and Anseriformes for ducks) with distinct physical and behavioral traits. A well-trained AI would consider these differences and avoid applying duckling data directly to chickens.

2. Training data often includes various animal categories. It's unlikely that an AI trained on diverse information would lack complete knowledge about any specific animal like chickens. Even if direct chicken data is limited,the AI could draw inferences from related bird data or general animal knowledge.

3. Hallucinations are not a normal behavior for AI models. AI outputs are based on their underlying algorithms and training data. They may present incomplete or inaccurate information, but "hallucinating" entirely new concepts beyond their training is not typical.

4. Transformative outputs can be achieved without resorting to such extreme assumptions. Even with limited data, an AI can analyze other relevant information, identify patterns, and make reasonable inferences to provide helpful or insightful answers, potentially exceeding user expectations.

Therefore, while this metaphor raises interesting points about the limitations and challenges of AI learning, it presents an overly simplistic and inaccurate characterization of how AI models deal with unfamiliar information.

Follow-up question for Google Bard: ChatGPT-4 made the following statement [submitted]. Do you agree or disagree with that?

Google Bard’s answer:YES, I agree with your analysis of ChatGPT-4's statement.

You accurately identified the strengths of the metaphor:

• Highlighting the generalization issue: AI models do often resort to drawing parallels from their training data to fill gaps in knowledge. This can lead to insightful predictions in some cases, but also to misleading outputs when the parallels are too weak.

• Illustrating the risk of "hallucinations": When extrapolating beyond their training data, AI models can generate responses that seem plausible but are ultimately inaccurate or even nonsensical. This metaphor effectively portrays this phenomenon.

• Emphasizing the importance of specific information: While general knowledge can be helpful, having accurate and relevant data on the specific topic is crucial for reliable AI outputs.

Overall, the metaphor effectively captures a real limitation of AI models and serves as a valuable reminder of the importance of responsible development and deployment of these technologies.

However, it is important to remember that metaphors are simplifications and have limitations:

• Specificity vs. generalizability: The duckling and chicken analogy focuses on two very distinct animals, while many real-world situations involve more subtle differences. AI models can often handle nuances within certain domains even with limited specific data.

• Agency in "hallucinations": The term "hallucinations" suggests a deliberate act of deception by the AI, which is not the case. It's more accurate to say that the AI simply makes the best predictions it can based on its incomplete knowledge.

Therefore, while the metaphor is valuable for raising awareness about AI limitations, we should avoid taking it too literally and interpret it within the context of broader discussions about AI capabilities and responsible development.

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Follow-up question for Google Bard: Follow-up question for Claude: Create a better metaphor.

Google Bard’s answer:Instead of the duckling and chicken analogy, here are a few alternative metaphors that I believe better capture the limitations of AI systems:

1. The Jigsaw Puzzle: Imagine an AI as someone trying to complete a vast jigsaw puzzle, but with some crucial pieces missing. While they can use the existing pieces to make educated guesses about the overall picture, the missing information can lead to gaps and inaccuracies. New data is like receiving additional puzzle pieces, allowing the AI to refine its understanding and fill in the blanks. This metaphor emphasizes the importance of data completeness and highlights the potential for bias or errors when working with incomplete information.

2. The Translation App: Think of an AI as a sophisticated translation app that can interpret and even generate different languages. However, its accuracy and fluency depend heavily on the quality of the training data it has access to. Incomplete or inaccurate training data can lead to mistranslations and misunderstandings, similar to how an AI might misinterpret or misrepresent information not explicitly covered in its training. This metaphor focuses on the role of training data quality and the risk of bias or errors from relying on imperfect information sources.

3. The Sculptor: Picture an AI as a sculptor working with a limited amount of clay. They can mold and shape the clay to create impressive figures, but their creative expression is ultimately constrained by the material at hand. Additional data is like receiving more clay, allowing the AI to expand its repertoire and refine its creations. This metaphor emphasizes the limits of AI's own inherent creativity and its dependence on additional information to fully unlock its potential.

Ultimately, the most effective metaphor will depend on the specific context and message you want to convey. Remember, the goal is to provide a clear and relatable illustration of AI limitations without oversimplifying or misrepresenting its capabilities.

I hope these alternatives offer you some inspiration!