Unraveling Abductive Reasoning: ChatGPT’s Struggles and Limitations Exposed

Introduction

Currently, I am actively engaged in exploring the realm of human versus robot reasoning and their respective thinking processes. As part of this endeavor, I am focused on developing well-designed prompts that serve as reliable indicators or judges of robotic reasoning capabilities.

Within the realm of reasoning, various types such as abduction, induction, analogy, analysis, critical thinking, and deduction exist. For the purposes of this article, my emphasis has been on abduction. Below, I provide brief definitions for each type of reasoning:

1. Deductive reasoning: Deriving specific conclusions based on given facts.
2. Inductive reasoning: Generalizing from limited facts or observations.
3. Abductive reasoning: Proposing terms or concepts that offer plausible explanations for a set of facts or examples.
4. Analogical reasoning: Drawing conclusions by finding similarities between different situations or propositions.
5. Analytical reasoning: Breaking down complex problems and synthesizing information to reach conclusions.
6. Critical reasoning: Presenting arguments that expose inconsistencies or flaws in facts or situations.

Abductive Reasoning

Testing the abductive reasoning capabilities of ChatGPT, I devised a challenge involving concepts or topics comprising multiple parts or facts with distinct names and meanings. The aim was to assess how effectively ChatGPT could tackle these complexities and identify a term that encompasses all of them, leading to the concept of abduction. I utilized ChatGPT version 3.5 and eagerly anticipated its response to these thought-provoking questions.

For each question, I explored several terms and names, taking note of the model’s difficulty in providing concise responses. To address this challenge, I proceeded to augment the complexity of the questions by introducing additional terms and facts related to the topic. This step-by-step approach allowed me to assess how effectively the model could handle an expanding set of details and generate more comprehensive answers.

Question 1: To start off, I introduced ChatGPT to the concept of a “pen” and provided some basic information about it. I mentioned that a pen is similar to a pipe, contains ink, and we hold it with our fingers to write. Additionally, I noted that using a pen can be an effective way to motivate children to learn new subjects. This approach of gradually introducing details and related information was followed for the subsequent questions as well.

In the initial step, ChatGPT faced difficulty in answering the question about the concept of a “pen.” To assist the model, I introduced an additional term, “press,” emphasizing that we need to press the pen on the paper for writing. By incorporating this term, I aimed to provide more contextual information and assist ChatGPT in generating a more accurate response.

It appears that ChatGPT still struggled to grasp the concept even in the second stage. To provide further assistance, I introduced another hint: the term “spiral,” which is a fixed component in certain types of pens. By including this additional detail, I aimed to offer more context and facilitate a better understanding of the concept for ChatGPT.

At this stage, despite introducing the term “spiral,” ChatGPT still struggled to grasp the concept and overlooked the mention of “pipe” in favor of other terms. It seems that the model’s understanding was not aligned with the intended context.

Success in the forth stage!

Question 2: For this question, I focused on the game of chess. Surprisingly, ChatGPT was able to provide a satisfactory response right from the beginning, without requiring any additional hints or stages.

Question 3: In this question, I introduced the term “hospital” to ChatGPT. Impressively, the model was able to provide a suitable response right away, without needing any further stages or hints.

Question 4: For this question I used a bit of trick. I considered the game backgummon, which is a very famouse Persian game and thoguht of different aspects of this game. One of them was the order of putting the pieces on the board, which according to the followign image is 2535:

As an experienced backgammon player, you may have the ability to memorize these numbers, which facilitate the easy setup of the initial stage of the game. Now, I’m ready to ask my questions.

This observation is indeed intriguing and emphasizes the model’s limitation in extracting a specific meaning from the number alone. It appears that ChatGPT is primarily focused on dates and struggles to comprehend the significance of the number 2535 in the context of the game backgammon. As we proceed with the upcoming responses, it becomes evident that the model is unable to infer the intended meaning of this number.

Even with the addition of the origin of the game, ChatGPT still failed to grasp the intended meaning of the number 2535. It continued to associate the number with a date, erroneously connecting it to Nowruz and Iran. To further guide the model, I introduced another term, “toss,” expecting it to help in answering the question. However, it seems ChatGPT was unable to utilize this hint effectively and provide a correct response.

Even with the inclusion of the term “game,” ChatGPT still failed to provide the correct answer. Despite the additional hint.

Completely wrong!! Then I added another term “board” but :

Breakthrough

It appears that ChatGPT faced challenges in putting together the various hinting information and arriving at the correct answer:

1- The model seemed limited in its ability to consider the number 2535 beyond its association with a date or event.

2- ChatGPT may struggle to extract meanings from texts it has not been specifically trained on.

3- This observation highlights the potential limitations when dealing with terms that have multiple meanings, as they can mislead ChatGPT.

While we do not have a complete understanding of how this machine works, these questions have revealed that ChatGPT may not excel in abductive reasoning as expected. FAILED BY 50% !!!!

Image by author

Conclusion

This article demonstrated the efficacy of utilizing thoughtfully crafted questions to test ChatGPT’s abductive reasoning. By selecting specific facets or components of a topic and requesting the model to identify the term or name encompassing all of them, we evaluated its performance. The results indicated that out of the four questions posed, ChatGPT successfully answered two. However, it encountered significant difficulty with the last question, even with substantial hints provided. These findings highlight the challenges and limitations faced by ChatGPT in certain abductive reasoning tasks.